{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BOW model and Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Table of Contents\n",
    "* [BOW model and Naive Bayes](#BOW-model-and-Naive-Bayes)\n",
    "\t* [Rotten Tomatoes data set](#Rotten-Tomatoes-data-set)\n",
    "\t\t* [Explore](#Explore)\n",
    "\t* [The Vector space model and a search engine.](#The-Vector-space-model-and-a-search-engine.)\n",
    "\t\t* [In Code](#In-Code)\n",
    "\t* [Naive Bayes](#Naive-Bayes)\n",
    "\t\t* [Cross-Validation and hyper-parameter fitting](#Cross-Validation-and-hyper-parameter-fitting)\n",
    "\t\t* [Work with the best params](#Work-with-the-best-params)\n",
    "\t* [Interpretation](#Interpretation)\n",
    "\t* [Callibration](#Callibration)\n",
    "\t* [To improve:](#To-improve:)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Rotten Tomatoes data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>critic</th>\n",
       "      <th>fresh</th>\n",
       "      <th>imdb</th>\n",
       "      <th>publication</th>\n",
       "      <th>quote</th>\n",
       "      <th>review_date</th>\n",
       "      <th>rtid</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Derek Adams</td>\n",
       "      <td>fresh</td>\n",
       "      <td>114709</td>\n",
       "      <td>Time Out</td>\n",
       "      <td>So ingenious in concept, design and execution ...</td>\n",
       "      <td>2009-10-04</td>\n",
       "      <td>9559</td>\n",
       "      <td>Toy story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Richard Corliss</td>\n",
       "      <td>fresh</td>\n",
       "      <td>114709</td>\n",
       "      <td>TIME Magazine</td>\n",
       "      <td>The year's most inventive comedy.</td>\n",
       "      <td>2008-08-31</td>\n",
       "      <td>9559</td>\n",
       "      <td>Toy story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David Ansen</td>\n",
       "      <td>fresh</td>\n",
       "      <td>114709</td>\n",
       "      <td>Newsweek</td>\n",
       "      <td>A winning animated feature that has something ...</td>\n",
       "      <td>2008-08-18</td>\n",
       "      <td>9559</td>\n",
       "      <td>Toy story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Leonard Klady</td>\n",
       "      <td>fresh</td>\n",
       "      <td>114709</td>\n",
       "      <td>Variety</td>\n",
       "      <td>The film sports a provocative and appealing st...</td>\n",
       "      <td>2008-06-09</td>\n",
       "      <td>9559</td>\n",
       "      <td>Toy story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jonathan Rosenbaum</td>\n",
       "      <td>fresh</td>\n",
       "      <td>114709</td>\n",
       "      <td>Chicago Reader</td>\n",
       "      <td>An entertaining computer-generated, hyperreali...</td>\n",
       "      <td>2008-03-10</td>\n",
       "      <td>9559</td>\n",
       "      <td>Toy story</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               critic  fresh    imdb     publication                                              quote review_date  rtid      title\n",
       "1         Derek Adams  fresh  114709        Time Out  So ingenious in concept, design and execution ...  2009-10-04  9559  Toy story\n",
       "2     Richard Corliss  fresh  114709   TIME Magazine                  The year's most inventive comedy.  2008-08-31  9559  Toy story\n",
       "3         David Ansen  fresh  114709        Newsweek  A winning animated feature that has something ...  2008-08-18  9559  Toy story\n",
       "4       Leonard Klady  fresh  114709         Variety  The film sports a provocative and appealing st...  2008-06-09  9559  Toy story\n",
       "5  Jonathan Rosenbaum  fresh  114709  Chicago Reader  An entertaining computer-generated, hyperreali...  2008-03-10  9559  Toy story"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critics = pd.read_csv('./critics.csv')\n",
    "#let's drop rows with missing quotes\n",
    "critics = critics[~critics.quote.isnull()]\n",
    "critics.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 15561\n",
      "Number of critics: 623\n",
      "Number of movies:  1921\n"
     ]
    }
   ],
   "source": [
    "n_reviews = len(critics)\n",
    "n_movies = critics.rtid.unique().size\n",
    "n_critics = critics.critic.unique().size\n",
    "\n",
    "\n",
    "print \"Number of reviews: %i\" % n_reviews\n",
    "print \"Number of critics: %i\" % n_critics\n",
    "print \"Number of movies:  %i\" % n_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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j/fXXzztQea211orDDz98qS6/cePGccQRR+Q+btSoURx++OExffr0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a543e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = critics.copy()\n",
    "df['fresh'] = df.fresh == 'fresh'\n",
    "grp = df.groupby('critic')\n",
    "counts = grp.critic.count()  # number of reviews by each critic\n",
    "means = grp.fresh.mean()     # average freshness for each critic\n",
    "\n",
    "means[counts > 100].hist(bins=10, edgecolor='w', lw=1)\n",
    "plt.xlabel(\"Average rating per critic\")\n",
    "plt.ylabel(\"N\")\n",
    "plt.yticks([0, 2, 4, 6, 8, 10]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##The Vector space model and a search engine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the diagrams here are snipped from\n",
    "See http://nlp.stanford.edu/IR-book/ which is a great resource on Text processing.\n",
    "\n",
    "Also check out Python packages nltk, spacy, and pattern, and their associated resources.\n",
    "\n",
    "Let us define the vector derived from document d by $\\bar V(d)$. What does this mean? Each document is considered to be a vector made up from a vocabulary, where there is one axis for each term in the vocabulary.\n",
    "\n",
    "To define the vocabulary, we take a union of all words we have seen in all documents. We then just associate an array index with them. So \"hello\" may be at index 5 and \"world\" at index 99.\n",
    "\n",
    "Then the document\n",
    "\n",
    "\"hello world world\"\n",
    "\n",
    "would be indexed as\n",
    "\n",
    "`[(5,1),(99,2)]`\n",
    "\n",
    "along with a dictionary\n",
    "\n",
    "``\n",
    "5: Hello\n",
    "99: World\n",
    "``\n",
    "\n",
    "so that you can see that our representation is one of a sparse array.\n",
    "\n",
    "Then, a set of documents becomes, in the usual `sklearn` style, a sparse matrix with rows being sparse arrays and columns \"being\" the features, ie the vocabulary. I put \"being\" in quites as the layout in memort is that of a matrix with many 0's, but, rather, we use the sparse representation we talked about above.\n",
    "\n",
    "Notice that this representation loses the relative ordering of the terms in the document. That is \"cat ate rat\" and \"rat ate cat\" are the same. Thus, this representation is also known as the Bag-Of-Words representation.\n",
    "\n",
    "Here is another example, from the book quoted above, although the matrix is transposed here so that documents are columns:\n",
    "\n",
    "![novel terms](terms.png)\n",
    "\n",
    "Such a matrix is also catted a Term-Document Matrix. Here, the terms being indexed could be stemmed before indexing; for instance, jealous and jealousy after stemming are the same feature. One could also make use of other \"Natural Language Processing\" transformations in constructing the vocabulary. We could use Lemmatization, which reduces words to lemmas: work, working, worked would all reduce to work. We could remove \"stopwords\" from our vocabulary, such as common words like \"the\". We could look for particular parts of speech, such as adjectives. This is often done in Sentiment Analysis. And so on. It all deoends on our application.\n",
    "\n",
    "From the book:\n",
    ">The standard way of quantifying the similarity between two documents $d_1$ and $d_2$  is to compute the cosine similarity of their vector representations $\\bar V(d_1)$ and $\\bar V(d_2)$:\n",
    "\n",
    "$$S_{12} = \\frac{\\bar V(d_1) \\cdot \\bar V(d_2)}{|\\bar V(d_1)| \\times |\\bar V(d_2)|}$$\n",
    "\n",
    "![Vector Space Model](vsm.png)\n",
    "\n",
    "\n",
    ">There is a far more compelling reason to represent documents as vectors: we can also view a query as a vector. Consider the query q = jealous gossip. This query turns into the unit vector $\\bar V(q)$ = (0, 0.707, 0.707) on the three coordinates below. \n",
    "\n",
    "![novel terms](terms2.png)\n",
    "\n",
    ">The key idea now: to assign to each document d a score equal to the dot product:\n",
    "\n",
    "$$\\bar V(q) \\cdot \\bar V(d)$$\n",
    "\n",
    "This we can use this simple Vector Model as a Search engine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###In Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original text is\n",
      "Hop on pop\n",
      "Hop off pop\n",
      "Hop Hop hop\n",
      "\n",
      "Transformed text vector is \n",
      "[[1 0 1 1]\n",
      " [1 1 0 1]\n",
      " [3 0 0 0]]\n",
      "\n",
      "Words for each feature:\n",
      "[u'hop', u'off', u'on', u'pop']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "text = ['Hop on pop', 'Hop off pop', 'Hop Hop hop']\n",
    "print \"Original text is\\n\", '\\n'.join(text)\n",
    "\n",
    "vectorizer = CountVectorizer(min_df=0)\n",
    "\n",
    "# call `fit` to build the vocabulary\n",
    "vectorizer.fit(text)\n",
    "\n",
    "# call `transform` to convert text to a bag of words\n",
    "x = vectorizer.transform(text)\n",
    "\n",
    "# CountVectorizer uses a sparse array to save memory, but it's easier in this assignment to \n",
    "# convert back to a \"normal\" numpy array\n",
    "x = x.toarray()\n",
    "\n",
    "print\n",
    "print \"Transformed text vector is \\n\", x\n",
    "\n",
    "# `get_feature_names` tracks which word is associated with each column of the transformed x\n",
    "print\n",
    "print \"Words for each feature:\"\n",
    "print vectorizer.get_feature_names()\n",
    "\n",
    "# Notice that the bag of words treatment doesn't preserve information about the *order* of words, \n",
    "# just their frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_xy(critics, vectorizer=None):\n",
    "    #Your code here    \n",
    "    if vectorizer is None:\n",
    "        vectorizer = CountVectorizer()\n",
    "    X = vectorizer.fit_transform(critics.quote)\n",
    "    X = X.tocsc()  # some versions of sklearn return COO format\n",
    "    y = (critics.fresh == 'fresh').values.astype(np.int)\n",
    "    return X, y\n",
    "X, y = make_xy(critics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This discussion follows that of HW3 in 2013's cs109 class.\n",
    "\n",
    "$$P(c|d) \\propto P(d|c) P(c) $$\n",
    "\n",
    "$$P(d|c)  = \\prod_k P(t_k | c) $$\n",
    "\n",
    "the conditional independence assumption.\n",
    "\n",
    "Then we see that for which c is $P(c|d)$ higher.\n",
    "\n",
    "For floating point underflow we change the product into a sum by going into log space. So:\n",
    "\n",
    "$$log(P(d|c))  = \\sum_k log (P(t_k | c)) $$\n",
    "\n",
    "But we must also handle non-existent terms, we cant have 0's for them:\n",
    "\n",
    "$$P(t_k|c) = \\frac{N_{kc}+\\alpha}{N_c+\\alpha N_{feat}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MN Accuracy: 77.23%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.cross_validation import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(X, y)\n",
    "clf = MultinomialNB().fit(xtrain, ytrain)\n",
    "print \"MN Accuracy: %0.2f%%\" % (100 * clf.score(xtest, ytest))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 0.92\n",
      "Accuracy on test data:     0.77\n"
     ]
    }
   ],
   "source": [
    "training_accuracy = clf.score(xtrain, ytrain)\n",
    "test_accuracy = clf.score(xtest, ytest)\n",
    "\n",
    "print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n",
    "print \"Accuracy on test data:     %0.2f\" % (test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly this is an overfit classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Cross-Validation and hyper-parameter fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `KFold` instead of `GridSearchCV` here as we will want to also set parameters in the CountVectorizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "def cv_score(clf, X, y, scorefunc):\n",
    "    result = 0.\n",
    "    nfold = 5\n",
    "    for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times\n",
    "        clf.fit(X[train], y[train]) # fit\n",
    "        result += scorefunc(clf, X[test], y[test]) # evaluate score function on held-out data\n",
    "    return result / nfold # average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the log-likelihood as the score here. Remember how in HW3 we were able to set different scores in `do_classify`. We do the same thing explicitly here in `scorefunc`. Indeed, what we do in `cv_score` above is to implement the cross-validation part of `GridSearchCV`.\n",
    "\n",
    "Since Naive Bayes classifiers are often used in asymmetric situations, it might help to actually maximize probability on the validation folds rather than just accuracy.\n",
    "\n",
    "Notice something else about using a custom score function. It allows us to do a lot of the choices with the Decision risk we care about (-profit for example) directly on the validation set, rather than comparing ROC curves on the test set as we did in HW3. You will often find people using `roc_auc`, precision, recall, or `F1-score` as risks or scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def log_likelihood(clf, x, y):\n",
    "    prob = clf.predict_log_proba(x)\n",
    "    rotten = y == 0\n",
    "    fresh = ~rotten\n",
    "    return prob[rotten, 0].sum() + prob[fresh, 1].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll cross-validate over the regularization parameter $\\alpha$ and the `min_df` of the `CountVectorizer`.\n",
    "\n",
    ">min_df: When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets set up the train and test masks first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "itrain, itest = train_test_split(xrange(critics.shape[0]), train_size=0.7)\n",
    "mask=np.ones(critics.shape[0], dtype='int')\n",
    "mask[itrain]=1\n",
    "mask[itest]=0\n",
    "mask = (mask==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#the grid of parameters to search over\n",
    "alphas = [0, .1, 1, 5, 10, 50]\n",
    "min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n",
    "\n",
    "#Find the best value for alpha and min_df, and the best classifier\n",
    "best_alpha = None\n",
    "best_min_df = None\n",
    "maxscore=-np.inf\n",
    "for alpha in alphas:\n",
    "    for min_df in min_dfs:         \n",
    "        vectorizer = CountVectorizer(min_df = min_df)       \n",
    "        Xthis, ythis = make_xy(critics, vectorizer)\n",
    "        Xtrainthis=Xthis[mask]\n",
    "        ytrainthis=ythis[mask]\n",
    "        #your code here\n",
    "        clf = MultinomialNB(alpha=alpha)\n",
    "        cvscore = cv_score(clf, Xtrainthis, ytrainthis, log_likelihood)\n",
    "\n",
    "        if cvscore > maxscore:\n",
    "            maxscore = cvscore\n",
    "            best_alpha, best_min_df = alpha, min_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha: 5.000000\n",
      "min_df: 0.001000\n"
     ]
    }
   ],
   "source": [
    "print \"alpha: %f\" % best_alpha\n",
    "print \"min_df: %f\" % best_min_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Work with the best params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 0.79\n",
      "Accuracy on test data:     0.73\n"
     ]
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(min_df=best_min_df)\n",
    "X, y = make_xy(critics, vectorizer)\n",
    "xtrain=X[mask]\n",
    "ytrain=y[mask]\n",
    "xtest=X[~mask]\n",
    "ytest=y[~mask]\n",
    "\n",
    "clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain)\n",
    "\n",
    "# Your code here. Print the accuracy on the test and training dataset\n",
    "training_accuracy = clf.score(xtrain, ytrain)\n",
    "test_accuracy = clf.score(xtest, ytest)\n",
    "\n",
    "print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n",
    "print \"Accuracy on test data:     %0.2f\" % (test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might be less accurate bit we are certainly not overfit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1087  758]\n",
      " [ 505 2319]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print confusion_matrix(ytest, clf.predict(xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the strongly predictive features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good words\t     P(fresh | word)\n",
      "         beautifully 0.91\n",
      "                rare 0.89\n",
      "             delight 0.89\n",
      "            touching 0.88\n",
      "        entertaining 0.88\n",
      "         brilliantly 0.86\n",
      "          surprising 0.86\n",
      "          remarkable 0.86\n",
      "               witty 0.86\n",
      "         masterpiece 0.86\n",
      "Bad words\t     P(fresh | word)\n",
      "            tiresome 0.22\n",
      "           pointless 0.22\n",
      "               fails 0.21\n",
      "      disappointment 0.21\n",
      "       disappointing 0.18\n",
      "               bland 0.18\n",
      "          uninspired 0.18\n",
      "                dull 0.17\n",
      "                lame 0.16\n",
      "       unfortunately 0.14\n"
     ]
    }
   ],
   "source": [
    "words = np.array(vectorizer.get_feature_names())\n",
    "\n",
    "x = np.eye(xtest.shape[1])\n",
    "probs = clf.predict_log_proba(x)[:, 0]\n",
    "ind = np.argsort(probs)\n",
    "\n",
    "good_words = words[ind[:10]]\n",
    "bad_words = words[ind[-10:]]\n",
    "\n",
    "good_prob = probs[ind[:10]]\n",
    "bad_prob = probs[ind[-10:]]\n",
    "\n",
    "print \"Good words\\t     P(fresh | word)\"\n",
    "for w, p in zip(good_words, good_prob):\n",
    "    print \"%20s\" % w, \"%0.2f\" % (1 - np.exp(p))\n",
    "    \n",
    "print \"Bad words\\t     P(fresh | word)\"\n",
    "for w, p in zip(bad_words, bad_prob):\n",
    "    print \"%20s\" % w, \"%0.2f\" % (1 - np.exp(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see mis-predictions as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mis-predicted Rotten quotes\n",
      "---------------------------\n",
      "It survives today only as an unusually pure example of a typical 50s art-film strategy: the attempt to make the most modern and most popular of art forms acceptable to the intelligentsia by forcing it into an arcane, antique mold.\n",
      "\n",
      "Benefits from a lively lead performance by the miscast Denzel Washington but doesn't come within light years of the book, one of the greatest American autobiographies.\n",
      "\n",
      "It's a sad day when an actor who's totally, beautifully in touch with his dark side finds himself stuck in a movie that's scared of its own shadow.\n",
      "\n",
      "Walken is one of the few undeniably charismatic male villains of recent years; he can generate a snakelike charm that makes his worst characters the most memorable, and here he operates on pure style.\n",
      "\n",
      "For all the pleasure there is in seeing effective, great-looking black women grappling with major life issues on screen, Waiting to Exhale is an uneven piece.\n",
      "\n",
      "Mis-predicted Fresh quotes\n",
      "--------------------------\n",
      "Some of the gags don't work, but fewer than in any previous Brooks film that I've seen, and when the jokes are meant to be bad, they are riotously poor. What more can one ask of Mel Brooks?\n",
      "\n",
      "There's a lot more to Nowhere in Africa -- too much, actually ... Yet even if the movie has at least one act too many, the question that runs through it -- of whether belonging to a place is a matter of time or of will -- remains consistent.\n",
      "\n",
      "The gangland plot is flimsy (bad guy Peter Greene wears too much eyeliner), and the jokes are erratic, but it's a far better showcase for Carrey's comic-from-Uranus talent than Ace Ventura.\n",
      "\n",
      "There's too much talent and too strong a story to mess it up. There was potential for more here, but this incarnation is nothing to be ashamed of, and some of the actors answer the bell.\n",
      "\n",
      "Though it's a good half hour too long, this overblown 1993 spin-off of the 60s TV show otherwise adds up to a pretty good suspense thriller.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x, y = make_xy(critics, vectorizer)\n",
    "\n",
    "prob = clf.predict_proba(x)[:, 0]\n",
    "predict = clf.predict(x)\n",
    "\n",
    "bad_rotten = np.argsort(prob[y == 0])[:5]\n",
    "bad_fresh = np.argsort(prob[y == 1])[-5:]\n",
    "\n",
    "print \"Mis-predicted Rotten quotes\"\n",
    "print '---------------------------'\n",
    "for row in bad_rotten:\n",
    "    print critics[y == 0].quote.irow(row)\n",
    "    print\n",
    "\n",
    "print \"Mis-predicted Fresh quotes\"\n",
    "print '--------------------------'\n",
    "for row in bad_fresh:\n",
    "    print critics[y == 1].quote.irow(row)\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02111976,  0.97888024]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict_proba(vectorizer.transform(['This movie is not remarkable, touching, or superb in any way']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Callibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probabilistic models like the Naive Bayes classifier have the nice property that they compute probabilities of a particular classification -- the predict_proba and predict_log_proba methods of MultinomialNB compute these probabilities.\n",
    "\n",
    "You should always assess whether these probabilities are calibrated -- that is, whether a prediction made with a confidence of x% is correct approximately x% of the time.\n",
    "\n",
    "Let's make a plot to assess model calibration. Schematically, we want something like this:\n",
    "\n",
    "![callibration](callibration.png)\n",
    "\n",
    "In words, we want to:\n",
    "\n",
    "- Take a collection of examples, and compute the freshness probability for each using clf.predict_proba\n",
    "- Gather examples into bins of similar freshness probability (the diagram shows 5 groups -- you should use something closer to 20)\n",
    "- For each bin, count the number of examples in that bin, and compute the fraction of examples in the bin which are fresh\n",
    "- In the upper plot, graph the expected P(Fresh) (x axis) and observed freshness fraction (Y axis). Estimate the uncertainty in observed freshness fraction F via the equation \n",
    "\n",
    "$$\\sigma = \\sqrt{\\frac{F(1-F)}{N}}$$\n",
    "\n",
    "- Overplot the line y=x. This is the trend we would expect if the model is perfectly calibrated\n",
    "- In the lower plot, show the number of examples in each bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "calibration_plot\n",
    "\n",
    "Builds a plot like the one above, from a classifier and review data\n",
    "\n",
    "Inputs\n",
    "-------\n",
    "clf : Classifier object\n",
    "    A MultinomialNB classifier\n",
    "X : (Nexample, Nfeature) array\n",
    "    The bag-of-words data\n",
    "Y : (Nexample) integer array\n",
    "    1 if a review is Fresh\n",
    "\"\"\"    \n",
    "#your code here\n",
    "\n",
    "def calibration_plot(clf, xtest, ytest):\n",
    "    prob = clf.predict_proba(xtest)[:, 1]\n",
    "    outcome = ytest\n",
    "    data = pd.DataFrame(dict(prob=prob, outcome=outcome))\n",
    "\n",
    "    #group outcomes into bins of similar probability\n",
    "    bins = np.linspace(0, 1, 20)\n",
    "    cuts = pd.cut(prob, bins)\n",
    "    binwidth = bins[1] - bins[0]\n",
    "    \n",
    "    #freshness ratio and number of examples in each bin\n",
    "    cal = data.groupby(cuts).outcome.agg(['mean', 'count'])\n",
    "    cal['pmid'] = (bins[:-1] + bins[1:]) / 2\n",
    "    cal['sig'] = np.sqrt(cal.pmid * (1 - cal.pmid) / cal['count'])\n",
    "        \n",
    "    #the calibration plot\n",
    "    ax = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "    p = plt.errorbar(cal.pmid, cal['mean'], cal['sig'])\n",
    "    plt.plot(cal.pmid, cal.pmid, linestyle='--', lw=1, color='k')\n",
    "    plt.ylabel(\"Empirical P(Fresh)\")\n",
    "    \n",
    "    #the distribution of P(fresh)\n",
    "    ax = plt.subplot2grid((3, 1), (2, 0), sharex=ax)\n",
    "    \n",
    "    plt.bar(left=cal.pmid - binwidth / 2, height=cal['count'],\n",
    "            width=.95 * (bins[1] - bins[0]),\n",
    "            fc=p[0].get_color())\n",
    "    \n",
    "    plt.xlabel(\"Predicted P(Fresh)\")\n",
    "    plt.ylabel(\"Number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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MDKxduxafffZZVb8EIiIiomorPT0dkZGRWLJkCdq3b4+vv/7aaLXIFUokP8zHvdQ83EvN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\n6R0LDyIiIiIi0jsWHkREREREpHf/B3ACvkNcyposAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aca5650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calibration_plot(clf, xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is still slightly over-confident when making low P(Fresh) predictions. However, the calibration plot shows the model is usually within 1 error bar of the expected performance where P(Fresh) >= 0.2. Finally, the model makes less-conclusive predictions on average -- the histogram in the calibration plot is more uniformly distributed, with fewer predictions clustered around P(Fresh) = 0 or 1.\n",
    "\n",
    "To think about/play with: What would happen if you tried this again using a function besides the log-likelihood -- for example, the classification accuracy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##To improve:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many things worth trying. Some examples:\n",
    "\n",
    "- You could try to build a NB model where the features are word pairs instead of words. This would be smart enough to realize that \"not good\" and \"so good\" mean very different things. This technique doesn't scale very well, since these features are much more sparse (and hence harder to detect repeatable patterns within).\n",
    "- You could try a model besides NB, that would allow for interactions between words -- for example, a Random Forest classifier.\n",
    "- You could consider adding supplemental features -- information about genre, director, cast, etc.\n",
    "- You could build a visualization that prints word reviews, and visually encodes each word with size or color to indicate how that word contributes to P(Fresh). For example, really bad words could show up as big and red, good words as big and green, common words as small and grey, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better features\n",
    "\n",
    "We could use TF-IDF instead. What is this? It stands for \n",
    "\n",
    "`Term-Frequency X Inverse Document Frequency`.\n",
    "\n",
    "In the standard `CountVectorizer` model above, we used just the term frequency in a document of words in our vocabulary. In TF-IDF, we weigh this term frequency by the inverse of its popularity in all document. For example, if the word \"movie\" showed up in all the documents, it would not have much predictive value. By weighing its counts by 1 divides by its overall frequency, we down-weight it. We can then use this tfidf weighted features as inputs to any classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#http://scikit-learn.org/dev/modules/feature_extraction.html#text-feature-extraction\n",
    "#http://scikit-learn.org/dev/modules/classes.html#text-feature-extraction-ref\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidfvectorizer = TfidfVectorizer(min_df=1, stop_words='english')\n",
    "Xtfidf=tfidfvectorizer.fit_transform(critics.quote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtfidf[0].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15561, 22126)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtfidf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do an unsupervized learning analysis of text as well. Algorithms like LDA are especially good for this purpose. we use the gensim library for this purpose. \n",
    "\n",
    "Install it with conda, not with pip.\n",
    "\n",
    "`$ conda install gensim`\n",
    "\n",
    "on the command line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    }
   ],
   "source": [
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(min_df=1, stop_words='english')\n",
    "X=vectorizer.fit_transform(critics.quote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "corpus=vectorizer.get_feature_names()\n",
    "id2words = dict((v, k) for k, v in vectorizer.vocabulary_.iteritems())\n",
    "corpus_gensim = gensim.matutils.Sparse2Corpus(X, documents_columns=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda = gensim.models.ldamodel.LdaModel(corpus_gensim, id2word=id2words, num_topics=5, update_every=1, chunksize=1000, passes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'0.013*film + 0.008*movie + 0.005*funny + 0.005*director + 0.005*does + 0.005*screen + 0.004*great + 0.004*best + 0.004*films + 0.004*performances',\n",
       " u'0.012*comedy + 0.010*film + 0.006*romantic + 0.006*movie + 0.005*little + 0.004*best + 0.004*story + 0.004*funny + 0.004*american + 0.003*characters',\n",
       " u'0.020*movie + 0.015*film + 0.007*like + 0.006*movies + 0.005*does + 0.004*don + 0.004*good + 0.004*best + 0.004*make + 0.004*kind',\n",
       " u'0.017*movie + 0.010*good + 0.010*film + 0.009*like + 0.007*time + 0.006*just + 0.005*fun + 0.005*doesn + 0.004*lot + 0.004*makes',\n",
       " u'0.015*film + 0.012*movie + 0.007*director + 0.004*story + 0.004*war + 0.003*self + 0.003*original + 0.003*makes + 0.003*just + 0.003*effects']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how each document fits in with the topics. You will see this in the homework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Generative Models redux.\n",
    "\n",
    "LDA is a generative model.\n",
    "\n",
    "When we talked about generative models earlier, we said that we'd need to model P(x|y), the features belonging to one class. And in general, we might want to model the input feature distribution P(x). How do we solve either of these problems? These fall under the rubric of density estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "\\cal D": {},
     "\\cal E": {},
     "\\cal H": {},
     "\\cal L": {},
     "\\ell": {},
     "\\mathbf #1": {},
     "\\mathbf x": {}
    }
   },
   "source": [
    "### Density estimation and Unsupervized learning\n",
    "\n",
    "$$\n",
    "\\renewcommand{\\like}{{\\cal L}}\n",
    "\\renewcommand{\\loglike}{{\\ell}}\n",
    "\\renewcommand{\\err}{{\\cal E}}\n",
    "\\renewcommand{\\dat}{{\\cal D}}\n",
    "\\renewcommand{\\hyp}{{\\cal H}}\n",
    "\\renewcommand{\\Ex}[2]{E_{#1}[#2]}\n",
    "\\renewcommand{\\x}{{\\mathbf x}}\n",
    "\\renewcommand{\\v}[1]{{\\mathbf #1}}\n",
    "$$\n",
    "\n",
    "The basic idea in unsupervised learning is to find a compact representation of the data $\\{\\v{x}_1, \\v{x}_2, ..., \\v{x}_n\\}$, whether these $\\v{x}$ come from a class conditional probability distribution like those for males or females, or from all the samples. In other words, we are trying to *estimate a feature distribution* in one case or the other. This is, of course the fundamental problem of statistics, the estimation of probability distributions from data. \n",
    "\n",
    "We saw an example of this where we used the maximum likelihood method in logistic regression. There we were trying to estimate $P(y|\\v{x}, \\v{w})$, a 1-D distribution in y, by finding the most appropriate parameters $\\v{w}$. Here we are trying to find some parametrization $\\theta_y$ for $P(x|y, \\theta_y)$ or $\\v{\\theta}$ in general for $P(x)$. \n",
    "\n",
    "But the basic method we will use remains the same: find the maximum likelihood, or, choose some probability distributions with parameters $\\v{\\theta}$, find the probability of each point of data if the data had come from this distribution, multiply these probabilities, and maximize the whole thing with respect to the parameters. (Equivalently we minimize the risk defined as the negative of the log-likelihood). \n",
    "\n",
    "Consider our heights and weights problem again. Suppose I did not tell you the labels: ie which samples were males and which samples were females. The data would then look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"https://dl.dropboxusercontent.com/u/75194/stats/data/01_heights_weights_genders.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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K7YKTqwKNpzVe43wiC3/xcnhkAPA//+f/5K/9tb925c9/5Vd+hQcPHvDxj3/8ws9+4id+\ngr/zd/7O01+hEEIIIc540rSS/YW5UmqXxmO1otl6mm1PpQsmlT670L+ECz3OX73zfn5hPk7nHX82\nLrafp/3373wEpc6k/8gwMHFXPDIA+OhHP8ov/dIvnbltu93yyU9+ku/+7u/mgx/8IL/2a7/GdDrl\nc5/73Jn7vfPOO7d/tUIIIcRr4Gnzwp8m9WVcmFe25L2jBh8izdbjfM9sZkicDvjaz/HPf084H9m0\nns5FljNDSunaFKYxr98PQYMPPctCoc4VBD+PfPnx/a9qd+FEQop/xV3xyABgsVjwR/7IHzlz2z/6\nR/+Ioij42Z/9WZRSfOELX+AP/aE/dOF+QgghhLjcbeWuX/aYmxYXV6akUCov+EOPMSX04MPp4n9S\nada1O+1bXyi6Lu+eW1OQEmxaD8Ofbx1OL329de3ouoApC0xZ4GLPetPxxr0pcLEG4ln15H/RfOhx\nIbGq3Wv33sSr5bFqAL70pS/x7/7dv+PTn/409+/fB+ALX/gC3/md3/lMLk4IIYR4XT1O7vpNPW5x\nsTUl85ml2QZcyC04F7Y603rT6GKXJrNuHJXVLLCklHCxx7WRxdQ88vVO6w0y7yJJDYXIw8986KmG\nAOJ5LIyfd3eezkdcyCcONzk1EeJZeqy+V//0n/5TPvKRj/BjP/Zju9v+z//5P3z961/nR3/0R/nu\n7/5ufviHf5j/+B//461fqBBCCHEXdD6yqh2rYef9sR57RXHxI6XEdKKZVZrZ1OwW//uFwC70PDzZ\n8u7Dlm8+qBmb9fvx5z7ifWTduF2e/75LF7kp7boQkfJ1XvV+n+YzuUo1DCVTSqGUunRA2W16ou9G\niGfkxicAv/d7v8ev/Mqv8A//4T/c3fbNb36T4+Nj/t//+3/81E/9FAcHB/zn//yf+bt/9+8C8KM/\n+qO3f8VCCCHEa+pp24OO9nfWJ/bR/9RbXfLGssSFuCvqHRfDncv5/kcnLc73JAWdC6cpQQpICRQY\nXZ5ZyO9fs7Ul67qjCz1WFyzmFufO7r7bIeC47PTgST+T69KhnvVpw/7ru1sKXIS4DSpd15Nr8JnP\nfIb/8B/+A//9v/93jMlHfV3X8b/+1//iO7/zO3nzzTd39/0bf+Nv8JWvfIVf/uVffqyL+c3f/E1m\ns9ljPUaItm0BmE6nL/hKxKtGfnfEk3iWvzf19vJF4tg+cswhh9y55/wAKx966m3Eh9N/2o1WzCfl\npcOu8n17fMz3N2V+zvH1mm3gvZVn20XG1YLRoHXJrCqZVwV1158pFjb67HOM1+xDv7uu+aTAh7R7\nL2bvvZxvlXndZ3L+/Y/PqUik3VxhrvzMnpX9axn/3m63mFLtfnee5/WIV1vbtqSU+L7v+75beb4b\nnwD8t//23/ihH/qh3eIfoKoq/sSf+BMX7vun/tSf4ld/9Vdp21b+YRVCCCFuwfkFZf7vHqOLMz8L\nsWfM0RkX1i4kzCX/4lutgOLMz6xWu+druh7FuKAGrRW6LJjZHADMJiVN11G73Ed/ZgtA0XSnQ69O\nF/mnr5OA2aQgbSM+piEA6S8s6n3od881Bic3/Xzqrr/wmKs+h2dh/1ogv/8tKbdfRRb/4sW60f8M\nvva1r/HlL395l9oz+t3f/V1+/dd/nY9//ONYezqko+s6JpPJEy3+v+u7vuuxHyPuts9//vOA/O6I\nxye/O+JJPMvfm/PpLuOgKoBN4zC6xNpyl6evlLpQzDoW6tphkq5zERRXdug5nyYDp2k268bhXMTF\nHvq0e803Dqe74Vxvbbq9WoHcW385NWcmAJ9f6I7Xvarz80NOATqY29NOQMNn8S2hZ904vItYU7KY\n2zP3G51v67luHArFYmYuvO7z6DJ0WZvRL37xi8wnpfx/jnhsn//852ma5tae70YBwO/8zu8A8LGP\nfezM7d/4xjf4mZ/5Gd555x1+6Id+CMiV7f/1v/5X/tgf+2O3dpFCCCHE62q1Vzi7nFsmlc65981p\nwWvqEy709CkNi0q9CwLOF5JWptwtqvcXzqvaXbpwPp8Hv9or4q1MzutXKifUuNCfWYCvardb6Ocu\nNz0KcLrE+R5ry3NJOMPzDotwq4szHYf2awDOvK+USCS6EFncLHM5fw6+P3uj4lZqLG70+pd0Gcon\nLkK8eDcKAL74xS9y//59Dg4Oztz+x//4H+d7vud7+PSnP83JyQlvvfUWv/RLv8QXv/hF/v2///fP\n5IKFEEKI18WqcazWHZB3+9e1Y7mwLGeWlMCUBXXr86J/aJtpdV7gW13sFtKb1rNpPACLmaGyJZth\n994ObTy7LrAGqsOLp/Pni1XHHftxca8KxXJmr9wxH+93tNrmHX9TYsv8mkWRF70PVlt8iMwnhjfP\nXcP+ScX518jvocTqXHRszeXFwucX3NaUVJXe1Sfs7/yfee/PaPjXZTMNJOVHvCxuFAA8fPjwwuIf\noCgKPvvZz/KZz3yGf/bP/hnHx8d89KMf5Rd/8Rf5w3/4D9/6xQohhBCvk3Hn3/nTDjyb2kF/diGe\nKawpcjK+Ou3Us24cD45bfMi73Z0LfOs7C4wp0eXZBedlLTTPpx2l4XrGRb01JQeL6tJF8rjodnsB\nhNYFrgtQaegTqlB0IbKpHT5EXBfoXORgYZlPDC70dHs78btd+cv69F/SIWh/gT2enox/v3D/59x2\nc/90pfNxV9B8vkuSEM/bjQKAT3/601f+7PDwkJ/5mZ+5tQsSQgghXjU3nb57FRf6izcO2SLGlrgu\n4EOkMnlBv9xL5dnUbrhv2j1uUzsWM3thAa04TfEZr3Ndu921j/UFPvS72oPrpglDngKslGIxs3Qu\nULeBpovMJyXWao5OtngfcKHH+4jW+XUXM5sDH5Wfy+7tmo81Bj70bF048/PKlpe2B51Ueve4S6/3\nOQ//Gt1We1chbstzqoUXQgghXk9Ps7hbzu0uBWi0mFmsKUkkKqvxtaN1Ibe0VIrKalKfdrvIXeix\nw05/03qaLl/PG4dTUkq7XX+lcteZsTB1telwsWdduyEXv6TrAs4PXWpMeaNgpjLlkLKU2DSJroP5\nRJ/WDfjTYWIjH/q88E7sHnvVc1eH00sDrNUlA8euS+e5LC3neSzCn2fqkRA3IQGAEEII8RSeZnF3\nMBt2uWOP85HFzLKY5q41la12efFGl5iy2G+3v3tdlRJHm45m6/FD0as9qPI1VJpqGAQ2phS50LOp\nXZ7eGyKzidkVDQN43/PGvQkpJVa1Q3F1MDAuzJ2P40BfrC1zCpHNO/ZHqy0AbRdICooETRdRBTw8\naVnMbH7cXrrT+V352xzYdf65nvb0RohXkQQAQgghxDN02QLz/G0f+dZ7l7bjfHjcsmkcJ5uO1Ces\nKfB+ygffnOdFd0rcP5hwtN6yqh1aFxzMK6aVYVM7KnParnNV565CXZcn+ZISXeiZFQpryt0O/Zhq\ns2k9D1dbvO+5v6wutN/cP/kwusD5SAg9ldEs59WuDanRBbosWMw0D046dKFYLitSTGxdYD4EPD70\nNz51GD+jp03neV6pOS8q9UiIq0gAIIQQQjyFRy3uLltgdj6S+nTmNrhYMLrtApvW8e5Ry4OjmhAT\ns5nF9z1vHEx2ufEAVpe8cW8KJA4Xp4vv89e0Ppc2s5ho6sblYmOlsLpgMc95+Q9P2uEEQtG5QEoJ\nP5xSwMUiZWvKXZrRyLnIGweT/BcFZalRJExZYrTC+55vPKwxumRiSr7l7cWNF9+3kc7zvFJzzj/f\nWMAtxIsiAYAQQgix53FTQh61EN1vrTnm4vvQny6Kx9c8t+gcH9e0gaNVS0yQ6Gm3ntWmoN56TFnu\n6n61LphP8j/pdtfCszizy1wNu+tbF3I3oUHu3a9yq01b4lxg3XpONh26KJgNO/T11lNvPSnl93O8\n3mJ0yXxqWMxtriMYXuOy1peLqYGUUEN1c+cjTesxpsAUBZ2LrGq3qw0ActXyXhvP62YYPMqLTvWp\nTLmbdCyLf/GiSQAghBBCDJ40JeRRC9H9Fpnj3/fbbD5K2s2NUhSFZjkzaF3wcNWxmGi6YRfeDjv4\nlc51AtaUvHnJ5N/l3O4W5M5HHqy2p3UHSrGpHXWbTwlMWdAnUMMKvNl6ZhND5wKbrWfrI5s2pxVt\nWscb96Ys5zZ3FRoKlK0td6cdlSlxpoRxsnHrUSphhms0w+yCzhUsh+5AYx2D1cVTpedc+b1Kao64\noyQAEEIIcac8aif4tlNCKlvyYEylIS9yFzNLdy4AuFD0OixM55VhOatoOo/RBabMu+Uq9fR9IgFN\nFyDlhfEb96YX8ui7YcDY/qJ8UzuOVlvqrefeosKZctdZSA87+WaoVXA+YkyZd/tnFuciwfeolAih\nh6HTT924PLF32LHfdoE0vrcEldVUlca5fBqynBlcyMXNxubBYfXW7wKU8cRkHGb2NN/FVd/rwdzu\nPh84215ViNeZBABCCCHujBfSjz0l0pjHklLOlYczffY7H3n/uAXyInTsDjSfG+YbgyKn+STg7Tfy\nFN1mG3Ah4mPPrNL0pN3E4M4N3XlCzO0+h4Aj9YmHq1xY7HyPj4Hff9cBirfvT/Gxx5QFoFAqt+h0\noefNgwnOR/o+4Tht6zmZGOaTfHqQgHXrWUzM7q27YcF+pjf/7HQQ1rp2JMWujam9xW4/NzHWY4yd\nl/bbqwrxOpMAQAghxJ1x3Q7/dSkh4zRXH/rdgh0e0SbT5aJZs58C5CJv3T9Nz1k17swsgNU6L9hJ\nOQ3nD7w9Z7P1OJ97599fTvLCWSeON3lA2KzS0MM3HtSEEDk8mOJCz/FqCySmlYEE3ke+9v6Gtosk\nEt7nwCTGHmMKZpXGpR7n8yCwxdTwrUNhbueHYCJEfCggd/ek3nrmU4M1+tJJw+PnNrYL7UJedFem\nZDEzrBtHiD2LmeXgXMpQ53I70cu+i8dx1fcq/fnFXSUBgBBCCDF4ZEHvcHrgQ48PidWmg0Jhh/78\nnY+X9sy3e4/ff85xUfz1BzWmyKcBLvT42MNmyxvLaQ4CdMkH38jDwerWs248fniuvGgvcwtNXVA3\nAd/3lNrRtB4fetZ1R6HzsC+lEj4ktluPGgpvIWF0wWrjmFUaP9QVmHOdhCpTcrCoqKxmYh1KqZye\nYwqs1ViTpwjvz/SytgSVh45tGkfne3wIzGfVkBZU8ua9KUqp3SnB+LmMKUOPKgJ+2u/1sgBAiLtA\nAgAhhBB3xk2KPq8q6B0Xiz7mFWnungN2WrBpXE6dUbkn/vkiUzv01h8X1O8ft6fDr1Kibn2e9Au5\n578CW5ang7u2nuXUAAprClLKt1tdoguFjwmjS9quBaU42ThCyLv8TRdYFAXeRbYucLCs0Lqg2QYe\nbjpi7PnQOwdYo6i3HqtL7t+b5rQcde6EZPhz3eRpv6ZUJKUg5R3980XAu78Pef8kqFtP00buLSs2\nrWMxsyildovy2xz6dd33ev73YfxOx25EchIgXlcSAAghhLgz9neC3bCLrlQ887Mn4XwccudPjUWm\nZ14v5a4368YNO9uaxczyjdrt5d/nnPj3jmt0WTKbGKwpWDee5dzmybnDNnsxNUAibAMntQOlmFSa\nEHpSSmxdZDmvMGVBjD3LRcWsyu04V61HJZhWJVordDHk/p/7vJzP7TkBXIh0XeThaghgytyByOpy\n17XHDgv/8fN8f6/9ad066m1AoZhOND5ErC5ZLqoz9Ri32bLzUc914fdh+H5SSs+nPkSIF0QCACGE\nEHfKuKAbF9GXLfb2c9aBYff+NAUIht37IXXHntshH+0vPvfvN3IuspgZFouKd48aFD0UeSc6hITW\nihD6/DJKsWnyjjlKDUv1vPN/OM/pQQcLSy7gzQO5ti4HJkYXvH1/wnxiOam3rOvEzJTogwkHc4su\nFCjF/YMK7/pcvDvPJxadC7vC5XXjdzn53kVC0dNsA2ZIAxqn+p4fbraqu2F+QA5yikLRdoHZRKMK\ndabLz/j43eyEGg4W1a4w+nHcpOj79Brd7ndi93ipBxCvKQkAhBBC3DmPKv4cF40u9HTDgjFPwgVV\nKExZ4GPPpNK7Bf2EHEicWeArzi4+XSCRe9qfzz9/Y1kBKQcdm44QIiEkVIL5zOTn1jkIeXDSomDX\nFjR34QEEmVwrAAAgAElEQVRIbFpP6CP3l3nQmCryzv54Xxci86ll3QYOVU4dUiQoCrQpmU8t1UG5\nm/rbuUgiL9x9GHfyIy70rFrHauOYTgzTieYwMaQlnW3ZuZxb1o0bFtqaRMKYghjye7rse3BDa84x\nZapuPO7wYovTp/mehbjLJAAQQggh9uym945/hsjRumM+0VRWM5uUQO7nv9/e8nyqyfnFZ2XKXU/7\nsR7A+8imzfcPoedktcXHlNN0qlzkOxbBWlOiVK4R8KHncFHhwnBKUeR0nPnEoJRiPjG4EPmOP3DI\npnUcrTraxqEU3JtX3F9UOJcX8u02EPue+cTgh0JmgOPVloRCl3mH3vmeEAOm1KDgZN3RdpGpLfFd\nwFV5vsBinmcFoE5TbpYzy1bnCcRjLYDXOU2pc4GHqy1GFxwscnHwunG705d8GtJjTckb9ybXpubs\nfw9uKGi+CRkKJu4SCQCEEELcOfuLvTHVZGIvtrF0IebF7LC73Wy3nNSeWXVxYXi+yPR8AGBN7taz\naT2b1pP63PrSucC67qiH9Jqw9bRd4GCZO/+ME34rW+JDZFoZTNnThcjJugMFh/MKMylQOZOHeuup\nt4EQ+mHBndBD2s1R3GJ1iSoUvu8xRvHOwZzZ1LJpHMerLVqXtF2e/Ot93oU3umBVR2aTgm88qPnm\nccN8ovGxZ1ppvO/PDPKqzOlnvD+B+MFJi/P5vZPSbtjYYmZJfUIV6kz3IR9yi9L9267axT+f8pOG\n7/dRQ9f2v7/9702KgMXrTAIAIYQQr5TbKBDddbOp3dBfX++696hCQcotLNdN7s9vTW55aWyJj7mV\n5plq2cuu63yHGR93C9K68egy5/TXjceYEhcCfQ8Ta1AKSApFwtrcZahuPfXWM50YjCk4XndoXRJT\nT9Pl2QTzmeFwMcHFHr/Z8qWvbkgoTJmLbunBp7gb9nV/UTGfGKpK880HG+rW42Pi7YnGxwLnA7Op\nJYSE0bmuYMyTn00M9Iltl4uTlRoKiNXw+SrFpsnBzluHUyaV3rX3XM5zy9B14zC6RKnTOgBSDhg2\nGzd89jl9yZrrd/IvBF26wIf+zNC1R/2+PKsOREK8bCQAEEII8cq4zUm+lSnpTHkxRSTBpNIolU8F\nUgIVFMbkQVpbFzE6BwTMLr+uVe3OxAfWlLjYk/o0nCgkvO9pO4cu8smAKUtSkXCxpzIFkOh8T9nl\n+gPv+3y/YSdckfPxjS25NzP4kO/vYh4AlmKebOtjpN32rBvHO4dTUAXW5hQka0qqSrOpO94/3pL6\nNAQjPSRou8B8aplPx3qHxPE6dyzy20AXekKfmDSeNz444QNvznetS9f1MNBsWNwfLCoO5vbSVBt7\nblf+rXvTXPMwTDNOsJvWC4+XmmPPTyIWQkgAIIQQ4tVxfofX+cim9Sxn9vJJvE94WrDrRz8sVjeN\np/MB7wK6UASfWNdu1+5yN+XW5534zgUYJumOz7NuTp8/qUTTeloXeOvehIRiNjU4H2mcw5qSECIu\nwqZ1zKeG+dAO1IVIvQ3DAj2iSLjKUJk8kMz7PEzM6ILZpKTpQJdFniYcegrV40PJfGJYzCx149g0\nHl0WpCLhQ+T9o5a3DqeoicUPJyS58Dh3A4oh0iuwtmBS5aVEiD2bJnfScaHfTfS1Og8cW9eO6nB6\nJtVmYvXpPITxsx++p4O5pTPD950bHl37PUoevxA3IwGAEEKIV9LYd14pdWUrz6tOC/bbfJ7v3rO/\nYByfa7P1vHfUMo65TUBS7Ba1bi/QOFq1u3x+W+Yi3nXtdjnxSZFbbcaEUgof4PDAwDBZ93Bh0brk\neL3N6Ug9NI3H6JJK58m6CujT8B8p0baeNz6wZDE1uWOPD3Qu1wv4kOgTzCqNKqBPuYA39aCU4r2j\nFl0qDpcV3dDzP8YeVRbMbC4otjafUihAm5KyKJgMaT7TKp8MbBrPwazCxZ6vv79hVmnuLaoLk5D3\n5RoHLl3cj0HY4wRxkscvxM1IACCEEOKVsb/DOy4o99NH9otDr2oBCafBwJiu4sd0mCsWjIuJYT7J\nRcI+JnSZsGXBw9WWzsehY06CBO+dbNGFwvTl6bCxQvHW4ZTOBY7XHa0LWF1wuJzupgRbU7KYGjaN\np27zKUC79ZAUPdC0HjXL/f9n01xUa0yBH4pczdBdqKo0FPDwuMXaAlVASmpX23Cy6YhA4wJ1F4ih\np0+5vWllhsFjOtcHjOlRzueOQSHmecXWFiil0bqgLEpI+ZTheN3iQoI+vybptAj3qsBsTLm6SVHv\nTVK+JI9fiOtJACCEEOKVsb+4V0rlXfYbtnkcXVYoqpS6Mk98vP98atBlwXQIOOqtp24cfZ9TX7oQ\nKYZi066LFApsV9JsA28Uis5HrNVYXWLnBdrkBfuYMrOc5UW91QV163ZtSLUB78H3icXUYE3JepjM\n+8Zyggv5swihp249aficvuXtBZ2LLGanbU03jUOXClIi+J5SF2itSOOxAvDO/enuJGHT+mFab4Ei\nzwPI95njQj4tCDFidIkxBZvG51OBiQYSIfaoomC50Cz3piJf9hlfGgDstfMcAz4feqrD6bXfsxDi\nahIACCGEeKWcz88/8zN7NpXnfBcepYaFuHn8wGExsyTcbqFcb8PuBKHpAs73xNRzfzGhUR7v82Cu\nxdQwnxpWm47KauZTzabxNI3nfdfkbj8Kgg8cHkwhJZqtZ9uFPGBrXgGKxdzyxsEkv49CcbzuuL+c\nsJxXu/fX92k3RKsyBW/en3Ews7x/3LLGQUsu8nWRdeNYTg2zqWExNWiTu/G8eW+Cc5GjTYd3EVSi\nIacOVabMwUOfKJSiMiVW59MHv9emczbUPoBiMTO5+Pemw7vO9fHPswJOg4bO5WBAdvmFeHISAAgh\nhHglXZfvvf/zMYVlN+G2CywXeeG8afJuuvNxV9R75nWGQMIOA61mVYktFdYU9H3Cu0hZFpg+UW8C\n6iDn06+GFqK5ODa38PS+HtpewqpxPFxtmdiSaaX56rsbvvZ+ndt8lgVvHU5zarwiFwT7nm+8v6Eb\npu0upnkC8KbJHYe6oWOO8/2QupNYD600N8NgrYktaVxEKZhWGm1yGpExJfOpQRWK1Ofe/DNb4krF\nyXpLsw3MZpb7lcbpAl87plZzb27zqYDKHYMqW2J0HpJmywIUvLVX+Lv/eZ7/jCF/JuM1j6ldm6FV\n6MgOQ9YkABDiyUkAIIQQ4pV1vlA095k/W0gKefhU58Kw81/m6b4n7dDXPhejrjZd3hWf2TOBwH4g\nUVnN4TzPDLi3nPCN92t8iIQQabuQ24cOBa3TyuSFeIJ162hbx3HtmU00JHj/uMGHngJA5YV336e8\niK4szkdOGse6ccys5oNvKVLMT+59RBUFxkcWU0NCUbceF3KPf+/zJF4XIg+OW1KfSH2iLApsoTBT\nw7wyea7B0LmoshrnIl08nVNgywJjNNPE6UTiobAZlU8TzDDgLKTEO4e5L6rzEe8jb55b/J//POE0\ncOt8ZLXpdq/TdYGq0nm+wJBa9SQpX0KIiyQAEEII8Uq7rtvPdkjPIbHLq7c6D/QyptwVqu5ShHTE\n6EcXm45Fwz7mIVyQ894V0DjPDM1iqjleO+rOs6k9ZamYTQxtG9BG5faXpsTHSFfnXfRyGKZ1Unc8\nPNnSdoGm9cRFhQ+Rg0XFbGidaTT4XtHpHlUotCk43nRonesBjCtzcIFiNjOE2OfPoVDcn1fMZ7mv\nfk1ec3cu4MfTg70hBkYX2FLjY9r19L+3sENb0nL47HrmVufTFR+ZT02uaSiLS9N1LivUvaw2wLk8\nIfj8rAZp7SnE05EAQAghxCttXTu27nTBbs1pisi4qLSmoOvykKxmHZlPNBSnq1y3l78+2j3HuQCj\n3ubnTCmBSvi+RyW4NzfoskSVCpLieOOoxyFeMXfaCb5nMinRZcFyZulj4qTekpJC64J7iwqUyu+p\nCzC0KC0KRbsNTIcUHJLCx4gpFTNyTYEit/WMwymBKfNcgGbrOVpv8226xGoFBSjUkDIUmU8MzkXq\nLkCfsLbElAV1m5/XGM18VpKAuvN5LsBwwlBvPSiYzSzOBUyZi6rt/snJI9J1xtObdeN2LV33nS8e\nltaeQjw9CQCEEEK8UvaLRFHDwnBMG9ktEs/+87YYhmz5bZ5Ma0xuecmw4PQh4lzEVvrM7vf+c0I+\nKVjVAR977q+26KLgYGZZbxz1NnK41Jgy71a3XU7DmVlNjLnV6Hrr0GYCJJbzihB61q0npZ5ppfmW\nN2e5mDZBn3JBb1EUNNt8grBuclvQg4XFh0i9HYZtDScZbx5OCD7iYu7Z32wDTetwIT+/8wFUiXWR\nNEnUQ5ef401E6zy3wA9BhwIWcwtp7HjksbZgPs35/WNb0WYbmE8Ntixwu8/pYkB11Xc5BlfjwDBV\nnAYBy8XFVCwhxNOTAEAIIcRL6/wQKODMbvy6dvjY47p8H2NL8HAwFPjuF/BaXTKfWipT5CLVIXd9\n7NVvbS6ETUMnnYNFHoy1btwu5z31iQS0LvL19za0nceHBCmhY+6GM5+aYVIYeaBWpTG6pOny9N5S\nF8yrnMbjCnjzXg4IppUGpfCxZzYpcDHn5LdbT+gTbx7kHfajvmfrPJNJifeJrQt8y5tzrCnQuqDt\nArPK5HaiXZ5IrFS+fquLHOwEjR+m9c4qw3HdkVIuNrYm1zg0bR4+NnbimU10LgaeDNFWgsXM7OoO\nIAcizsWzefoKVkPb0vO79/vB1Xhi4ELPwbySnX4hniEJAIQQQjwTjzPB9arHn8/t96E/kw/uh/xz\nY4qci+4Cla0uLeC1tsSanBbTDTn/DMWlH3xrAZzWCIxFp9su7HamN8N0X0j0MbfqbLd5sq/ReZzt\nunF5MVxAIuFCbslZFgWzSg9tPXNKy0ntcgpO6Ikh4n2PLsvdvIGYoG49bRexpSL0MDUF88qwad3Q\n7UdxMK/Q5d6Cey+DxuqCaaWZVprDRUW99axqh9UlR6uhJoBEiIngA03X89bhhLot2LQehSMNJwCL\nab5u5yN2ryvPcmZJKQ0zA/J0Y1vl4KoLkdSnXWrWdYO88jA2feVMBiHE7ZAAQAghxK17kgmu+48d\nc8L3+/W70PNwtWU+0bsF5bjWtbrcLUpVgveP213x6XJu84JSwWrdDc+VU36Wiyr3mR86ziyGwlil\n1F79QJl7/W8DTRdQQFHC1kU2W8+9RcXEGg7nltD3hD7SBziYGUwJX3/YUBnNcmYwZb5GH3t8iJii\noO0D69azqR0nteNDH1gwm1jmleHD7xzQHkZWTUeMPdbq/NiYqEyBLgsmE0PT5uBkNrPMp7ndqSoU\nC2t2PfrDULB8b1FhdEnd+twxKCRCjKyG4OV43fH+UctsWuI83JvnlJ/j9Zb51NJsI9bmwWWQT07W\njWPT5pOSxdTAcFJCji/OfJb7NQHXzXIQQjwbEgAIIYS4dY8z7fXMffYCh3FhPv5T1Q278W7Yja9M\nDgwWc7vLGVfDc/R9/vu2C6SUckpQgqrKaTU+9NhKk1LaFQvvp65U9rSAeNN61rXD6Ny5x8ecD3R/\naYepuoqigPnMoICj9Zb5JAcSLvQcLib5zSVY1VvqTlOViqQUq8az2Tpi6ClUfp62i/jYoVXBbJJ3\n7yc278j3/bBbH3Pq0tgWs9kGFsayHLrvdF0AlQOu1DhUURB8Ln4e03tQORCxpsi3dYHSlLiQ6GOP\nD2pII/LE2BNiGk5gFE3j8qmOLXPKVBdzCpHv2eChzScGdj/dZxjAtu98S9CxpuN8O1chxO2SAEAI\nIcQZT5u681SvvRc47C/MR1YXdCEXuLrQ51x+XewWluvGnX/KM+/H6mK3yB+Dhv3cc6XUmff8/nHL\nl3//iPUmL5iXM0Pn8yCte8sJs8pSd37XUGg+t7gQSSnn8m9dTu2JMebaAd9ji8DhvQnzScnxeku7\njcQQsZMSUxS4EJlYjdUFqsgdeFSZg4UYE3qoM5hWmsXcDC1JFfOpyd2Mhq5D3kcUiuW8wuqCbzxs\n6IbrSeTiZFWonA606ThYWEhDUXTMKVX35gYXwKcAOhdLQ65f2NQOlWA+NXnI2fAZ53kEeTryt72z\nPJOSBBd3+PdnOTzpqZEQ4vFIACCEEGLnJouwmwQIN03tuKzId7S/MB+vofMRUiKRUKhdt5oxZ39i\nNYp4oZXk+WuytsxpP8NrWFNSVfpMugrA0UnLqna7/vgu9GyavDvdbiNt5wkh0pM79VidC4zfPWqo\nG0+ICRdzbUKMiaJUVMYytSUJxWJi0UXB8WpLqYrdMC9bltxfzri3nHCy7iAl3rpneXDScVxvuT+f\ncn9Zocvc/ceYkqN1x6bpWEw1h8tp7t8/nBC40OOcH+YH5CDD94nDmeHNe1P6YZXetj738a801b2K\nFMGaPPW4bhwUik3rmVe5iHjTet44mOTBY5ALjGOugG6anNaUryGfFkwqfbrrf+67f1Q7VyHE7ZIA\nQAghxM51qTs33aW9atrrmee95LnG1pIja8pdR59tF9g0jk3rcwoLuSD1jcMpbx1Od8+ZcEPq0Om1\n7L/++H4qe3YIWEqnk2bHguMu9EytplQ9m9bxtfc2vPew5YOHU6Y259H3wMTmfPzO51x7U+b0nVXt\nKArQZYnzjqIo6VVehPuYmE0NSkGY2TxULPTMh9qGo02XPwpVsK47jmuPLhRv35sytRpdKnSZu/rU\nrR8+N0XdRmaT/B7H05OHJy0uJBQJNbx+jD0x5lagE1PmomKrmU0N84lhObdshs+5bh3OlvihvWc+\nLUnMJxbnIlrn1101+aRkWmnuLQ2JRDdMBD4YagY6nwujx9Maq0vWTS7AHgu8r2rnKoS4HfK/LCGE\nEGfk3eLT1phXtW3cv+3SU4BLpr2ef9wFCSaVvjJw8KGnaX1erI/1AJuO7t5093oHc8sazhQB73cF\nqkzJqnG5hajPi840vHbXBZzPQcjROk/idTFRtx2bJrBuHTEkHqw7fv/9mpQSMQbKouCr726GBWxi\nWhl0mdtvhpD3143RTExJHxPrxjOxeghcphyvW37vG2sqU1BZQ4yR1TpytOqYWs3RektlS3RZUreB\n945bDpcT3lhWOdWoi7RdYD61zCclkOsVcjpSXuSfbPL7mVrDbKhR0KWiB2YTw3w6dDstFJUud6ci\nqU9YnYeXvXfUYss8Kdjo3LFIKQVFHnxmtMKFnpQYrsXkwoxxTsMQ9K2b0yAtDfMOVFGc6fDUDa1Y\nhRC3TwIAIYQQpxRnds+7Ljz3riznA4cxVcT5XLwbY6RzPUEXzKeaNHTsObPIH04EVo3j/eOWTeNA\nKRZTgw+RbpgbMPawD7GnJ6fAgOJwmQuLZ7agaRMPVl0u1C0U04lGK8XRuuWtwxmbtqfpGkKfUIDW\nBS726KJg3XjaJlBVCqs1MSW0UvgQWcxsHsw1LNZDSMyHVpsPVt0wgTcviDetp/MBXRSEvqfZekyp\nMIWicXlRrVReTG9diVLwrW8vqUzJekgr0kUJKXK0ajleb1nM86nD/UWFXU5I5JaeSqldG85xt96H\nSGU13/aBJZ3LJw7zqd4FCZvacbiwgOJo3WHKYjdhuDLlMEAt7/pbXZwZFDbOYdgv0gaZ+CvEsyQB\ngBBCiFNp6IAzLMqqYcLs6DbbNt7kucYdYzcEAZ2LbNpcBwDgfGJ2Sb4/5MX/at2xaR1146md52uh\nZ9UEljPDbKJ5eNKRyIO83jyY4mIunD1adxwuKyDvphtd5t3zqWHTQTFU/fqQC3iP61wLEGPPZGII\nMbKo8gK/73sm1ZRJpfnmg5rKakKKTCYGlfLuflEUzGaGlFKuK/A9k0lJWeTgYD7RrFsPRb6WqTW5\nPanvaVrHtutZzAp0mYeQHa065hNLYwI+RGYTw2pTc7TeEmPPtCqZmJJV3dFuA5vWs5iaITVKn/ns\njS64fzDJu/RKsZzBwdyeSZlyoc8TgnUehlY3Hh/63feZGIquxxqLc99ZruVQZ4q0J5UsUYR4VuR/\nXUIIIc6wQwHmZW6S2z+6SbGwD/2FVJ39x7khRWcMSHyMbLYO7yJaF2hToDgbOIyP//qDGlMo6jZQ\nO0/TBFofCaHn99/tuH+QJ/CuG48tFXXn2XaB6cTkibyhx/nEvbklxCmb1lOVJRvA9z2mLGlaT93l\nRXZZKKw2ON+z2ri84w4UpuC9owZrCtqtp1SKyhiaJjA91LiYuD/L7TnfP26IfSKkngKNLksUifvL\nitlEoxToMi/I+wSdD3S+ZzbVVFYzqwy+j4SQ6ELkaN1xtGnRpcaHQAw9vk8oF3m42uYTnolhakv6\niWHduFwMzcUULWvKC6cD430Wc0OKeVG/GAKBolBUVu++QzgtvrZj+9DQ7yYzj0XY1/1eCSGengQA\nQgghdm6yK39dbj9cXyy8v7uc01byz7thYbh7nAuk4Z8qFyLNNjAxeffax9wJiKK4tEjZ+8BR43lw\nsmXrApMhncZoxXoTeXjScjCzFCSUKgkxzwuYWY2LPXUbgLxI7Xu4tzAcrTp87Lk3N0xsSesCKilK\nVaBSHn612nTYqmRiNL5P0CfWbcdEG/o+8u52Q9VYbAnOLQh95IiUW3/akqLM7T8nWlMOE4Bjgkll\niDGxmBnWrafoexZTQwg90NP5wP2DCW0dKEuVU2lSIiXFe+9vWCwqirKAPhc8dy6fQmhd5Lx/U+Jj\nz7p2u+Lo/Zz8C78re78HlS1Z1W6XvrNcVBwMAd2qdqctV3UBaJQ/Tdmy54q0hRDPngQAQgghdh5n\nh/9ROhcvFBMrFS88v9vbRR5TfKw57es/drKpTJnz+IciYRJMhtaf6tzrjs+bhj9NCZvQ0/SJg7kl\nhJ7FTLN1kaQUhwdTfIi7NJVxgm6I44K+YdN6bFmynFraOnG0dsyXntnU0BQepXqc72lbx9YHCq1o\nG8829sTUUypF7COhT3Q+UW+3ub2m1RwuJzw8bplPDEnlYOntwxkhBta1IxWKgrzDP60KphPNuu2g\nBK1L3jqcsq4dTeeHRXgY8vHzh7OYGNqJY1JpZhWs247ZtKLSisWswpYFi5nB2BLn8uC0zkc2TV64\nL2enA72uSvcai687c/H35nxQaXWxCw6EEC+GBABCCCHOeNQO/02HhDkfLxQTq0vuN6b2nL/N7u0O\nK6WGXWLNbKo53nS5haYtMabAlKcpQushSBh76S9mFlUolCpourxgL+iZTDSFUrs883XjmNpc1Gp0\nwRtDV6EHxy1f2mw5WrWgFArYukRZQA9sO0+fekJIw88V95YTCgVNiCgSBSq31Kxz56HoE5SKsih4\n97ih8/0wfCtP9+2GWgKt81yAEBNbo9lsHROtmQxdfnzoofdoXTKtNG/em6K14t2jyLoNTIZOP9su\nUpQFplS8eTBlObNok691VuXUI6NLvIu7z7vrAqYsqLeeh6uOyhS8cTh95KL9qt+b64LKFzl4Toi7\nSgIAIYQQN/K0k1rHTjDAmdaQI2vHheLZFKRdG081FpOedo6ZTzSLuWVduzz4She7kwQfIrNKc7is\nsLqk3t+FNgUk2DSOZht4+96E2cTsLsn5POzr/eOak03uIJRS4rjuWLeRqSnYdh5TFKiioBoW5V0X\nyU+tWG0coY/osmRSlfi+J/QJU+WZASjoY76G+czSbv3Qi7+k3gZUynn8ZVHgdE+7CZzQcTCdUFUF\nMSXQmtB7JtaQVEJrzbe9fUDjcj3D8aYjpcTUlGxd5OF6y8G84t5i2NVXCudyByFrclvPsUNPno2Q\nWAytPlOfdvUaj+uq4ECm/wrxYkgAIIQQ4kYeZwaANeUulQSG9X7idEJvyv3mJzb3/B+7yQBUtrq8\nGDTBcmo4tpoQE0YXVEZjdcGm9TkA2E0PjjTbPMTKtz3vdS0kOFxWLOeWfqgz+OCbc5zv2TQdR5vc\n7nJeaZRSrDYddRdRBXTbSAgR+lxDYE2RF+++Y2Yt7xzOKYoC+p73Tro8EbeL9CmhJor1xuH7nkLl\nVptFWeC7wNRqXB8JvqfeOnwEEyPGaEiJPibsvEANOfsp9cyqQFvD1JRMjaJ1ntZFHh4nplPDm4cV\n84mlJjGxhrJUkBKmLDC2ZFrlwuJN6wmhZz7RzKc2v08fqduAKXM///PF4Lc9mfdxfqeEELdHAgAh\nhBC3rrI5ALCmxIWehydbrClw4XShT4K3DqePnQJyOLfMbIkPkU3jcXGND5F1Y1hODYuZZTmzeN/z\n/knDV9/b4H3knfszUp/YNI7l0Maybj1KwbtHDc126DTkI60LuNDz7lFNWeYF/3rr6NrIdhvQCo42\nuaWmWhTMp4audbiYiDGx3UYSPVVl0EUuWJ6YEjMz9CmxWnfoUjHXBfcnhnYb6INiYgqUVpB6UIpC\nK1SfUGXOnQ89HNeOzkXmE82m7ZhNDH2vclBTKtq25N58gi5KFrPcNYg01EoUCqtzpyVjShh29ddN\nB8ByXmFNQedCDnjQOJ/beW4aQEmajhCvAwkAhBBC3MjjzAAYF4jr2tG5gNUq5+p3AdCnQQBn00O6\nvTSh/YVm5/MgsG8+bEgpEUKkC3nwlqsDs6mlbh1145g3Lt8ee2KfJ/E2beCrfs2bh4Hl1BJizx94\newnAV75+QjMMBgsxsm4jfewxZd6537QO53ucy/MIYp/Y+h7VBSaTEgVsGk9MEfpEn3rKUqEKjSLR\n9z1Fqfj/7L27r2TZWf/9Wdd9qzq37vEMvAbL6E0QCIEFfwCImAgRWcIBKQ6wkAiQHCASCBABwhKC\nEIlLTALCASKAkMSBES8/eRg8M93nUlX7tq5vsHZVn9PT0+4Z9/xmbO+PNJ5x1alT+9Rep/t51vo+\n36+tNSJBSJm21tSV4nJbI4oxD+fnZac+Iwgx0VhFs0iLhBC0yTAHx5OdI4bEYQyMU2DTBB5fNsXb\nv9LPPPcFeJ9xofxsknJ64ePys94L4yrBXiVD4Oqs5t3rnvfvJrRWdHU5jcgps+3sgzXw/Wr3X2eu\nxKiP8S8AACAASURBVMrKyquzNgArKysrK6/ER3UIqoxiXoZq77v9OBexWp4KvftJv8dTA+AD2vCc\nM7ML9FNgcoHGlnAurRSIEjzVVYa7pYEYJs84eQ69J6TE7DI+DvjzXHa/F4Y5IlLG58T1YWaeAyll\nrrZ1ufY5Lim/EmUyykn6KRLSTD8K1JUgLXIdACUFLmfmKSC1pNFAligEPkWkgPOuoWkk1kjeve7p\nGkvbGMbZ41zEVJr/53MbaqsJIWG1RGnBf37nFoCYoB/n5ecUyMyS4uyRwNPdhFElXCz4yNO7gbY2\nnG9bvI+L1r9crzEKf2/w+jB69r3nrLEYo7g9zICgss/yIY4zF/fvz/018qq8LteplZWVj8baAKys\nrKysvDIf1yHI3tvJRxQrz8qoktZ7KIWs9+lZYNRzhSEUpx6rJV4JpgzOJ/aD52IjGUaPVrIMwsoy\nDPz0duDJ7cj1bqCfIloLOq/JMdNWJYHXaoXWgsGnclIwlAJcSEAKpBCgBefWMs+RgGQ8FH18VUGM\nmd1hZhCBlDNto2ibYiEaUkIjis++gBATOUHbGqpKEAJ4mTDLLvv7NwP96PEps2k04+SptaaxmpvD\njDjOBChB0oKYSnMllrTglDIpw+Pa0FQKIxUhJ2JMnG1qGquXULNEztC1x5AuxbYxGK2wVnF9N+J9\nBCFAlOaD/LBpml+QEfBxtfuvkiuxsrLyelkbgJWVlZWVl/J8YQ8f3LH9UDeXexKPY8LwsfiffWR/\ncKeB3350S4MguNxWbFtLZZ/9NeVCKZYhLA4+HhBF6hIzbSUZxyKH8THhQ+JmP+N8QooiqxldRIiZ\n/3nvwOgCn3+8pasN4+QRCLpmCfiaPf3gAEGlJZXVVJXm0DtizkiRSTkTM4xzZNMqpJCIxW7TaIVV\nkgS0lUYZCUKgRTkBGZQkxsTdPiKV4LtPA093M5BRQuB84ulu4jAEKiOol1yCyXlmn4gpgyy6/0oJ\nfEwICdvWICRc301s25IeXNWGdnH8sVqxaSw+Jh6dN+zHEt51DOOyWuFDwhpBzgLvA8McGJ3nzav2\ndC/Wgn1l5QebtQFYWVlZWQFevIP/fGG/O8xLIVl2f4/P7ZfBVODk6DO7yFlXAqledDJwf3e/+N8n\n+inQ1RrnIvvsqJZmYZoD1fI9fUooLdFBYIxkmiPOBayRtLXGasUwBq7Oa/6/t2/pXUAkqG1JEJZC\n0tSKQ+/4z/mGxkomn8rwcGM5TA4mwc1hZtMYLrcNUoBUknH2aJGL+5ApxbJWEiXLkG6OmdGn0wyC\nkqLMA+SMFJKri5qbu5F+mJFa4WZPXdsiW8qZEDO6LkO84+zpR09OcHFekXzChUxOlFOFkNESNl1F\nbTVtVbINUoZtUzFOnsmVnAAtBV1tTs4+pblS5GxgyQs43p/L85r9XjBMDh8SRkk2neGYPbDtLHXF\na9PurzkAKyv/91kbgJWVlZWVB4V+8eufqZfd9/tSj9lHBOLBEO++d0yLa453EQbYdMWJ53siig9+\nP3qGORCXnXsfE9WS+HssCLddhQsjIZRrnUIgJThrDY/e2GCNIsTE+7cjRkt2fcDHxKYxOJeYQ6KB\nk5Rlcp44CXJrOAwO7zO1lSUNl4wg43wkpeLIUxnF5abiaW1IZC4vWna9p7aaptZoBbe7mSlEtJQo\nUXTzMeUSStYZaquRUqLJ1FoyCYEPkWlyKCmRojQRzocS9CUgpMTTmxHnEklmtBRctHU5HcmZtjYY\nCUpLhjngwsTVWU0Ggs9oBT4mQkxIKRgmj4/plD9w/17OLrJtLGQY5kDXWrrGsGksm7bkATwI8fp+\nE6PXHICVlU+FtQFYWVlZWXk2oHtvN/b47/uDuS98rY8IIXD3Crl9P7Pt7MsLPFFe60Nid3D0c+Dx\nec3FpvrAe1RGUV00OF+CtTIBFked0SW6WPzs27oihFgsPK97aqsRQmJUxC8BYW9etOz6mRgzSkmc\ni8wuMfsJqSXBRUKKJVwLgfMRJQQHH7k8b6isYPIZBTSVBjLRRYIspwTbJSOgOAEJlBQ0jUYv2QLT\nHDhMYZHrCGLK1I0hxYwUAp+KdKmpy/d2LhATxBiRQmJqhV68/betYdtVS/hZkTFJIbjdz2ir0ELQ\n1IauWgZ9tUArTU6Z6/3MfvRsGoPV5f5WVi0WqYZ81ZZU5ZhwIXIYHu7yvw7t/poDsLLy6bA2ACsr\nKyufMp8lCcQxuAs42UfuR8ejs7roxBct+X0qo0qY1+L5D8+Sdl9W4DlXbDONVrS1QT//N1LmA05B\n1/sJRKaxmnkOjIPDOU9bKc6WxqFtDNpHtAJtBPvBk0Upwq2WvHHV8c57e8gZrQQ+ZepKE4EUEkmA\n85mcHAhZ/PAFzC6xHz0hZba15vKioWss17sZ5zz9GNBKoiU4EnYZqr06t0A5WehHT6I0VeNUnI26\nxnBx0THOpYAXlAFipQTGakIqg8aTC2ybispqjJRIJZlDIu4njFI0dYWkNDUxZjJF9gSZnMEYiTbl\nQ3bLEO/tfuJmP3G5sXSNLfeNMqTtQ2I/FGmS0SXX4Rju9vwa/ThruOQPlBmQ+0FwKysrnzxrA7Cy\nsrLyKfJZkUA878fuYoKcTzvDzieEEKci+36xV1cwu/E05Ht8/Hmcj6fTgsqW+YLjawTgvGJwYQmr\nkmw7+8E5hFy87Scf8KEUuRmBD5nb3cym1VhTL8U0+ABX5y2VLa46b1w2dLXm4rwiuERIiXGOHEZH\nSgktBdZo6taymxyHYSanjBQAgpQF3mVqk8kxF4ed1lCdNxzGmbvDzDAHaqOL5McYGqvZjx7nM4OL\nSwqyJKaM1pKmMVRG0TWGN68aQPDd675IdXwkhIxWik2raCqNVRKpBLUpDcYwReYUqesyeCylop8W\nG9Cmpq0MXa0JsTRnxyYNSkaCUgLnM13N6d7et/xcfnSq5V49v0P/cdbw8TXHWZH7+RBrDsDKyifP\n2gCsrKysfIp8ViQQx/fzITG7iIDFcYciETHqgf77+evbbuzJzvNYKB4LuWkOD6RF1iqe3I68d92f\nbDF9jNz0DrtYUtpl/mDXu9Nu9bFwDSkxzZG61lSVQgiIObHvZyor6EfHzc5hjKa2gX7wy3MKFxP7\nYaLWCozm6d3IfpiYfOKsNiTK9xJSICjDtjFEnEu4GGkrQ6ak/QotyQgaKxmdp7EGX0WUkiglqSvN\n1aYiAVpGJj8zjp6YyyBvInFRVWzbiotNhdECqzW7wRX9fsiMc6DvHYMLNI2mqw3GarrGcrGpaGrN\nOEeEAB8i4xzpGsXji5ZNrZlDJMSErTQVZVB5P/jTvW4qTbtIgOzSbAkhSpAYYJed/+pec/c8H2cN\nn9bCc0FvZ0vTt7Ky8smyNgArKysrK8BS1HcWcNzspyKR0RIfFMao01DwkedlH29cts+KQfGsyBOy\n+Ob7ZQd6uBuZfCSnjPOJJ+8MaKM431gE8Pa7e7pG8+ajDTlnJhfYj4l5Dsw+lCFgHzBBcn5Wc9EV\ndxtjin3nu9cTwUdmH2jr0kwcRoeIibv9jPdluDZnqKzm8XnHODvmkAih2GqCYNNa0sGxd8XjnyyQ\nShCcwCVoK8O2sygp8SHiY0IpRSUE1iouulL8HwZHFsXeU2nFYT8hpUArSUoZo0GQ8RGmw8g4R1JI\naCXQWqGM5MxY2kqjxbMdeasV4xQxRkACXWmMluSUl3kITUuZgbBacrapqIxCiJHru5EY06kA/zD5\njbVq+dzjS093vh9Op0DPDRivrKx8cqwNwMrKysqnyPPSm+NjnwZHWUbOmbYx9P1cdt1zseqvrD7p\nv18k+6grzdkLBn+dixxGT0657ECPnn50TC5hlGA/epqUeOOywUjJrZ+52Tsy/cmB6HY/09aGYQqM\ni1uQCxGpJQhobbH+vNmXIdvJBUKC2kpEhhiKo0/XaOY5MrrAxXlVBnStJqWM1AkipMUBqKoMLJp3\nQaStJZvWsgtT0enHtMw5BIQUTGOgqSWzK5r7w+Do50ilJSlDPzlyzkgFUgoqrWgqxTgmvJ9446JF\nZIUoUwI0lWGYy+dtlKSpNNuunBRsG4PWAhfBhSJRerxpTp+50ZJh8hhTiv/a6tPQ7qOLZtnhn5h9\nQpDLiYcQbDfFAejZCYAESmDYUbr1fJH+cdbwZ2ndr6z8KLI2ACsrKyufIs974n+aQ8An9x8fsUrS\nS0nwkUqX3dmjXvs49HvU9HsfTw3C44vmwXNuef62d2gpGICndyM3+5kQEl1jOIyOEDRPrkfaxrDr\nHT4W/3ljynCoEIJdP/Pu05794AkuoLTAucBdTMTWchiKVGYcA8PsqbXkSR8RSpBCZvSRzIzIxQrT\nKIHcgHeZmFI54dBliNbHSHCBmHKRGQFVJamNYlLF/ae2GiVFcSFqKyprGJfQLC0zslaInNgNJbgs\nxZIGvK0qzs4sMSVmn7jtZy43ll3vaGrD+dYihcBouDuI0lS5gBSCupJsmoqfeHPLMHmsLrKmEEqK\nsjUKcmlAtJLkVIr5/ehO2n7nIznDxbbGhXKPAB5fNi9s7qyWL5XmfJw1/Fla9ysrP4qsDcDKysrK\np8zrsFP8JDBKlmJy8cN3IZ2u86jpdyEWNx9AIJjmxekmlR3kfvCl4FySaw+jZ3KRafakLJBzLHae\nIbIbihVoTGVnfZg87eI4dLGt+D/v7JjmeLIOVUkQk+fqvOEwOm4PjtkHYkikGHl3P9O0ms9tWg6T\nJxwmnuwddaVJKXHXO964bFEichgTznkGF7BGo7VAGclwKMX7o6sGmSFlcAk6KZhDIPaJ2Qdu7yZy\nTgxzoKo0QsA0JqSElBJCSOpK4ZefbQqRWiuaRsMS3jUvsw5w3O239FPCh8i2s2xqQ2U0Z5sKozVt\nvbgxjYEQEjeHmYtNhVzkTSFkmkaTgevbiU2j2Y+CfrH+7OpF+68VQn5wvuOjFvQfdQ1/Vtf9ysqP\nAmsDsLKysrICPJNlHHf4objskMEsWvDnZRrOR3yIOJ8IKbNpzeLVX6Q7LiRcTEwucNeXGYAYMlor\nvE/0k2dTa6SAfT8Rc2kklBKMLnDuI2897or9ZAhwlKoLGKdArBRk+N/3DswhQs7EDEoKYkzIBKMr\nu/IJicsRHROV0SgB17sJoQU3hxElihxIItgfZoxRXG1rtCyWpk93I0pJkk9MLpFTJuTEzW5m1zsq\nW+w35xCpjOTRWYPzgrNW0tQGNyc2tWeKZR5CZYExJaVXa4nSCiWLXOd8W7NpNCknhrEEmtW2yHe6\n2uBjIucyyJtzoq50ce7RimEKdI3G+8jdfmacA1oLoKZrDFZL+sGXZiOXe1QvrkzHE4D7xT+UYezj\n//+wov2zZGe7srLyctYGYGVlZWXlVLz5UOw+66rYRlYGEEVTXplSbEMZ3MzA7WHCh4yxJZzqVABW\nGuciIZWQL0QJspp9IonMxbYm+MjTu5Fh9AQSWiq6VjNMET9FYs64KXB1UXPeVaU4F4LGSN67Hola\nFFmRC8WtRkBKmd1uIoSS3utCpokJJSQhlkCvEBOCgNaG/Thzta059IFp8sjFftSqMrislSSITD94\nfMycbyy7SjH5xP8+7bFKcte70gx4gVSQc0JkzeVZy+TKiUc/ebSUWGswMiEon9U4B6rOcLmtyIhT\nnkHXaKxWXJ01WL0U30aiVZFDFS9Rln8EVsnyj5YELXn/ZiTlctrgQ+R679kfHJ9/c0vXGIBFBlWs\nRLvWMi3Dvnk5vXEh8fR2BAGbJTH4wyw+Pyt2tisrK6/G2gCsrKys/Ihzv3g7Dt3WlS5F/jIM+jyV\nVUuIlUXLZ4XfYfRIKehyxlpFWyl6JcgIQoi0VXGnEULw7vWAEMVpRqUyKLu78zStxGeJd57KWu72\njs9ddvzEj53zznu70qDUnipq6rpo3q/OK/opcBg9SkBA0DWGeQ685z1VZchZIFIiK0mIiX4KTDMc\nhjJYHHKCOTFPgaZWJQV4TpyfWUIISDK7w1RORBTc7ies0UuYmURIkFnQNJbGlgZKq+L2IyipxSlG\n7PLZVYv8BgEhZj7/uY7zJWfBh8Rh9Ghd5iB8iPRT5GKjePNRx+wCzkVsytzcTYQYCVMqqc1WM86e\nlJfMNiFKv7D0DS4ktq3FhcSmNg9CuPa9Y9MUV6Wj+w/kIgdbfPpfZPH5WbGzXVlZeTXWBmBlZWXl\nU+C+XIKlMINPRzrxocXb8+FgISFgkbuoU5MARQp0lApVuqQFH4bixd+1mtlnYlRoI2ibZ7vJJayr\nePMLKRmniSmW3e/ihSO4Ocz8z3t73rhoSnAW8Oi8IaWyK36zn5hc4uqyReuZ0QXkHKmtRKI5zGUm\noes0u30ihFhsOMnl+9+NIEWZHRAZ74tG/3JjcSFye3DEmDFKMM6ecU40VlA3hrbSpJhKIu9y4lE3\nZTj46d1YdtSBR2c1ZLgbZryLxAwxlZmKx+cN55tqyV0oi8H5xPVupK40RkmsVkiRsLYU4eRyGiNC\n4nJrOYyeEDM+ZnCBs86Whqp3NFbTWEUIGSHEkvEg2baWTHFpcq40Jqd7/YI14Vxc03pXVn5IWBuA\nlZWVlU+ID9NE399xPw7TVpV+qcTidb7/q3J/GLQU+BmznAocbT8fXzRMc2A/uFMTg1iKypCotERr\nhfOLDj0Xz3soA7VFy56xRnG7G5l8wiSPEgIhisVmXWneee/AO+8f0EaihaSpDD4Grg8TdWXYNOV6\nU2O42tbMVSRl6GqBGYs2XwLnXcXsAgjBpjWMYyDEZbo3Q0yZnBNkRcowTAEZAmeNLYm5ocwWpCRp\njaLREq8kPjhczlRW0RiNtYrgMyF4lJaEEKkqQ1sbrm8HyNA2FZWVVJVG63IqEUJpe6yVKFUGds83\nFVZJxBJesB9cGaC1in4ObNqKTVcV29HRgxCcbyr6wdPWBnKirexS4Gd8WN6ngnl61uDNc6CuH5YF\ndrFZfbAuXmDXudp6rqz8YLE2ACsrKyufAC/TRN/fcZ8XC8b7u6uvQzrxUTTZx+LNhXTa+d1u7Onr\nK6PY9e4DcqDZleRWKNIfoASA+cgwOO4OM3ejw/sICIwSaCkZ5sAbtaWtNd/5rmOOCZEzkyuWnVJK\nJh/YNJZEIuWMXGxAjVZcbKvivNPPtFaxqQ0hRWLKNLXmiobb/YjzZQ5AS4lVoLXCprSMNme8j8Rc\nsgh6l6iNxEoFSiElJxmMjhLq8rO1lSQHyeXW8viyLXMHPrFpLCEmNq0punufqKxCzpJhmDj0JcdA\nyVJNh5zxIRJTQqsZa8rPpZWALPAxcdFZnuwm9oNbpFmZtim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pasU7UvD2229TG7Gk8gbu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QcRaGviFF8ezfD56hb+hbIw3OJAFmRksIWM2FnMW1yBnNh6cJ5zWv9q2cYFQ50TBG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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a97b6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Weight, df.Height, '.', alpha=0.08);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data looks vaguely elliptical and has two \"clusters\". Besides we know that heights and weights have normal distributions associated with them. So we decide to fit these features, with no knowledge of labels, with a mixture of two 2-D normal distributions. \n",
    "\n",
    "$$P(x) = \\lambda G_0(\\v{x},\\theta_0) + (1 - \\lambda) G_1(\\v{x},\\theta_1) $$\n",
    "\n",
    "What we are doing is a probability distribution estimation on these height and weight features, by fitting for the parameters of whats known as a \"mixture of gaussians\". Note these are not the per label gaussians we fit before in LDA: rather, there are no labels any more, so this is just a mixture of gaussians. This is just a density estimation.\n",
    "\n",
    "At this point, you may object, saying that we know from generative classifiers that we can find $P(x)$ as:\n",
    "\n",
    "$$P(x) = \\sum_y P(x|y, \\theta_y) P(y).$$\n",
    "\n",
    "You are right, if you knew the labels. But remember, I have taken these labels away from you, and thus there are no $y$'s, and this formula does not hold any more.\n",
    "\n",
    "But your objection also makes sense: why not right the input density $P(x)$ as a sum of components, each of which is some other probability distribution. This is the notion of **clustering**: an attempt to find hidden structure in the data. So we can always write:\n",
    "\n",
    "$$P(x) = \\sum_z \\lambda_z P(x|z, \\theta_z),$$\n",
    "\n",
    "where $z$ is some **hidden** variable which indexes the number of clusters in our problem. This is a variant of the idea behind the famous **kmeans** clustering algorithm, which we shall encounter in class.\n",
    "\n",
    "So thats what we do below here, using two clusters based on our visual reconnoiter of the density in the graph above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMM(covariance_type='tied', init_params='wmc', min_covar=0.001,\n",
      "  n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None,\n",
      "  thresh=None, tol=0.001)\n",
      "[[  63.75414799  136.64386191]\n",
      " [  69.05054815  186.89700669]] [[   7.79123887   47.70252408]\n",
      " [  47.70252408  399.61407127]]\n"
     ]
    }
   ],
   "source": [
    "Xall=df[['Height', 'Weight']].values\n",
    "from sklearn.mixture import GMM\n",
    "n_clusters=2\n",
    "clfgmm = GMM(n_components=n_clusters, covariance_type=\"tied\")\n",
    "clfgmm.fit(Xall)\n",
    "print clfgmm\n",
    "gmm_means=clfgmm.means_\n",
    "gmm_covar=clfgmm.covars_\n",
    "print gmm_means, gmm_covar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we use these gaussians to assign clusters? Just like we did in the generative case with LDA, we can ask, which Gaussian is higher at a particular sample. We'll cluster that sample under an artificial label created by that cluster. \n",
    "\n",
    "We plot the results below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import linalg\n",
    "\n",
    "def plot_ellipse(splot, mean, cov, color):\n",
    "    v, w = linalg.eigh(cov)\n",
    "    u = w[0] / linalg.norm(w[0])\n",
    "    angle = np.arctan(u[1] / u[0])\n",
    "    angle = 180 * angle / np.pi  # convert to degrees\n",
    "    # filled Gaussian at 2 standard deviation\n",
    "    ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,\n",
    "                              180 + angle, color=color, lw=3, fill=False)\n",
    "    ell.set_clip_box(splot.bbox)\n",
    "    ell1 = mpl.patches.Ellipse(mean, 1 * v[0] ** 0.5, 1 * v[1] ** 0.5,\n",
    "                              180 + angle, color=color, lw=3, fill=False)\n",
    "    ell1.set_clip_box(splot.bbox)\n",
    "    ell3 = mpl.patches.Ellipse(mean, 3 * v[0] ** 0.5, 3 * v[1] ** 0.5,\n",
    "                              180 + angle, color=color, lw=3, fill=False)\n",
    "    ell3.set_clip_box(splot.bbox)\n",
    "    #ell.set_alpha(0.2)\n",
    "    splot.add_artist(ell)\n",
    "    splot.add_artist(ell1)\n",
    "    splot.add_artist(ell3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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btWsDxjJ7MBMTgVlSHsWNAoiIiIhocow2QK43o3A4kPIo11VfxeJh+1o4U0BEREREk6Ne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fzuxhtZs349YaAUCHDZhRcyVcljOi3XV6sZZyR/oq6mF7e0DHX6KRQMsS2X5W/5vOlf0OJBr1ef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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a141350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')\n",
    "plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')\n",
    "gmm_labels=clfgmm.predict(Xall)\n",
    "for k, col in zip(range(n_clusters), ['blue','red']):\n",
    "    my_members = gmm_labels == k\n",
    "    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',\n",
    "            markerfacecolor=col, marker='.', alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we know, a-priori, that two is the right number of clusters? We can try and fit a mixture of 3 gaussians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMM(covariance_type='tied', init_params='wmc', min_covar=0.001,\n",
      "  n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None,\n",
      "  thresh=None, tol=0.001)\n",
      "[[  66.27189886  162.17290849]\n",
      " [  63.29051219  131.78754676]\n",
      " [  69.69208734  192.39008464]] [[   7.0407902    40.79791591]\n",
      " [  40.79791591  335.28692429]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sqe1BM2qi5JbaNvuagXh+Nt6zdcdgkYueBASOrhyN4WAYUknsre9Fxa/AFS7q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SswWh/Fj338yGekNkKQ6AFoBbtAqlKe3vc0N3ztBlIn7chyQhWCAiUU0pIsqyxj+4u3s+jsRfQu\n6kUg8CKPONUpRlJKpJS0ohZ5M08QBgihmw6hBOcsO4fqZJVDBw51phMPDQ8xFA5BnU48KYq53Qjo\nDDjr6u9iyVlLWL14NYNLtRegv9BPl9uFFHq6cqISVKKouBX8yCdVKV6kZwVM+VPUj9fZde8u9u/f\nDxGdAl4aknNWnMP2S7ezcf1GkiRh96O7ieIITFi7bC0blm2g6lVJ7ZSeXA/HDh/jC1/6Ak8+/qR+\nnRIkki6zi7e+/a1cfd3VTIfTHaNwM2p2BsQV0gJFu0jFrdDldBGrGCHE3FyKRHSumfnXnxCik3jV\n9r0AHTN8ySl1JGcnPrZ9WzZcLCNjjqwpyMjIyMg4iXZKS7vIP1XKiyWtTgLR/McBC4qttmHzxGOm\n/KnOROJ2+hDomFEiyNt5/bPQEhuBIE7iTvJRlEaMN8b53ve/x4c/9GGSIEEgKJfKfOBPP0D/in4m\n/AlUqKfT0tS7HqP1UepBHT/2mfQmcUyHaqtKY6zBww88zN4n92otfYGOhAYX8oU8m7ds5tItl+pJ\nvVGdMAqRSEIVsn9yP37ik6YpXugRqxjXdDGkQT3SkqWqVyVv5VlUXsQV267AztkcPXAUb9qbm13Q\n9gYkgIKcmaN/UT+L+hfRu6qX3oFeIhFhWRau4RInMYtKi1hVWUVPvoex+hjHmsdo+k1sU39mrbiF\na7rUm3X2HtjLwzsfZmJkQv9uZvvpEs7beh7XX389ruPiRz6u6ZJzc+zbuQ/ZkqRuytbtW8nbee0F\nmIj45D9+kvvvux9VUKicQtYlRbPIr77uV3nrm99KaqUEcYBpaSO3F3n4iU/OyGFbNgkJeZnvXBfd\nbndHRjZ/9X/+NQRzjWfb+zL/2mr7ReY3pKdqCtrHnur+jIznG1lTkJGRkZFxStqF2XxONBCfqNU+\ncUpx+zwnSonqQZ3p1nTHCNqWBkVppNN/VIIUku5cNwCeoScX11o1ojRipjWDa7rcePONfPpzn0Yo\ngZKKxYOLef//ej/lvnLHK2AKk1bUwos9SnYJx3QYmhkiSiIMZTB0aIiHdj/EsfFjWqKjmFuhd2D1\nWavZtm0bmzdtJlQhy8rLaEUtgjggb+XxYg+Ufi+k0J4AoQSpSHGkQzNqMtGcwAs9LXmytFbedV1e\ndN6LCM4NODZxjJnqDEZi0O/0Y5s2pXKJVUtWISwt06q1alRbVf2epdo8G6QBi7sWs7S0FEMaDNeH\naQQNepwe8oYu3EMV0gpbPPb4Y9x1/11M1ab0B+GiG48INmzcwFWXXcWaFWvoyfUwOjKKZVjk7TzN\nqSaPP/w4RmpACi+59CVMT0xzw4038OMf/pg0SRGmIJUppmFy7XXX8t9+7b+xbMkyBIKZ1gymNFHo\n4XRJmmAIg95Cb+czR9FZ3S/aRZpR8yQJGrBgMN2JtBvWMAk7cw7O5Bo/nUn5dPdnZPyikjUFGRkZ\nGRmn5ER5RbtQaq/KtmcJtCUc7cSeNqczgDbCBvWwTiNodJoCgaBoFzs7B/35/gUZ8wrFRHOCaqtK\nwSngSpfPffZz3HSzNgKrnGLl8pX85Z/9JcsGl/H05NNEaaTNtU6Rql+l1qphCYuckYMYdj28i927\ndzNTm1mY3iOAHJy34Txe/KIX09vbS6QiUlIc0yFKImJD7wIAOIaDYzv4RZ9WpAeAVWVVp+bETZpR\nE8u0MFKDsl3Gla42aBsWzbDJotIiXNNlqjxFmIZ0OV305noxTIPUSEHBQG6AaqtKK25RbVVxDZfu\nXDdKKS1PigN6C73MtGb0a0DiGA4hITt+vIO7776b6ca0/qvvomVIEjYs2cC2F2xjWf8yegu9uJaL\nQBAkAY7l4EqXG791I0magITz1pzHjlt38K2vfUvPbGgbnk247IWX8cuv/mX9fjkRcRJTsLXXoJ3Q\nJBDkrByWYWFKk7yZJ2/r1KiBwkDn+nk2hfj863T+LI0gDhbM1TiTOR/PhdkgGRk/K7KmICMjIyPj\nlJworxBCLNg58GIPL/JYli4jiANaqgXMreaezlw87U9TC2pESdSZLdAeUKXQEajt54nTGCkkk94k\nYRKSt/KoVPHRT36UHbfv0CbaBNZvWs+73/1ucqUcfuDTm+slzaU61z7y8CMfwzA4fPgwP/zhD7n3\noXt1oZqiC1sXLRWS+uu6669jy4YtlNwSAkGr1aIZNTuNgCENbdhVMcP1YeI0JmfkOmk+YRLqRKY0\nJJUpSypLdM6+aRJFEbGKaQUt6o06jWaDRtAgCAOkIYmdGAODglvAwiJv5TneOI4XeRSdIpOtSaZa\nU6QqZXXPagaKAwgEBbNAT64HP/Z5avQpHt71MA/d+xDNWlP/brONgClN1m1cxzlrz6G/0o8jHWJi\nlNL+A6X0Z2JiMjk6yd0/uhtlK0QgOPT4IfY+tBehZpOS0pRtF2zjla97JWtXrsWQBgj9uYVJqKVb\n0iQ1U+IwxjZtusyujkldIDo7BG25mm3YODjPqPc/VZHf9iG0m4D2bsKJ8zJ+UlNwqtuypiDj+UDW\nFGRkZGRknJb58olG2CCIg07hVAtrmGLhn5Fm2HzGSMcF8w9Q1Fo14jSmkqvgmA45M7fgeDErtA9T\nXWQHUcCnPvsp7vzxnYiCQISCS7ddyjt/7526iDYs/MTXA8CSiFpYY3hmmHsfvJcH7nqA/U/v11Ij\npRBCYCgDV7g06g3dHEh41fWvYuu5WzEMg6JdpBE28GO/s4Iet2KEEhiGgVSSKW8Ky9AxqihdEPfl\n+0hViiUtqpNVJo9OMjI8wsGRg4zOjDJRncD3fL1D4aD/Gre92RJIoGJXWNm/ks0bN1M5q0LBKlAP\n6hjKIG/mMaWJIQwm/AnKThkkHHj6AF/9/le574n79GyF9vuYCkp2iZde8VI2vnAjtbQGCnpzvZTd\nMkdqR5j2pjvFuWM4SCSf+6fPafNxKpFNSRAHCClQhmLT1k385n/+TbqXdetpy9LURmoElmFhmzYo\nqOQqNMMmQaSnRlecCqB3nBzToTvX3fGwzL922sPzoiSiYBc6KUSnkqi15w+0d5cyMjKePVlTkJGR\nkZFxxpwoJzJN/WfEMqyFcwo4teQiTEIKdqFjOE1VqgdSSZN6UCdVKQWrQJiGerVazs4/UJAmKZ/+\n7KfZce8OXcAHcPUVV/PGt70R27TxY596UKeVtLSmvAU3fv1Gvn3Ht6lVa4hIIE2JQmH4BmevO5tr\nfukabr75Zryqh1KK7Rdt56KLLyKIgs5uhSEMDGHQCBra4JxEDKVDJElCzsnRl+uj5JSIo5gDUwfY\nf3A/1QNVhg4OMT4+jp/6cwlCLnMNQIqOF237F9pvn6l/rk5WeWToER558BGsisU1l1/DudvORXZJ\ngiTAEAZKKSbrkzy26zH+/od/z/6h/Toq1UJ/Kejp6eH6K67nkssuoSvfhSlM6mGdOI0xDZNG2KDL\n7aJkl7CE1RlSd9MXb+LJR59EIhEtgdEyUFKx8uyVvO233sbll11OlESMNEYIooDETDClSZfTRX+h\nn4H8QEfbbxs2S8pLMA2TKIkQQmBKs+MlOfF6aTePpjAxTZM0TZn0Jk/S+J8oUTtxB6EtR3uma/LE\nazZLJMp4vpI1BRkZGRkZZ0x7tRagy+6aK/qkDSa4ptuRgIBOGGrLL+ZLOjqPMfTPOTOnJ+rOHttO\nGRII/NgnZ+b41Cc/xZ0/uhPl6F2Gay+7lve+672M++P4idbzxyrm+MRxvrLjK9zzzXuoyzppLkXY\nAplIckmOi6+4mN94zW+w6bxNfOIfP8HIxAiYUCqUeM0rXkO32001rWJbNq2kRa1Vww99plvT2IZN\nM2zSilvExIzWRjnkH6I6WmVkaITJ45MQg+EZpCrV6T45FgwYIwW69O9ddIqQg8RMkHmJjCWNuIEy\nlX5MCMIXRBMR3/q3b/HoE4/y62/6dRKRMDY+xh0P3METDz5Bfaqu38sinaZi6ZKlbDl/C+efez6r\n+1Z3hn4tryzHCz1G6iNESg+IGywMYpkWtrTZ+eBObvjSDUyNTHXkWSIVLD1rKe/9vfdyxbVX0Iyb\nzLRmmPKmOj6B3nwvYRJScSsdg3jRLnb8JlGqn8sLPQRCm5ijJlEaLRhCBwvNvm2aYRPsk5uH0yUK\nOaZzUurVmTQF7cef6WMyMn5RyJqCjIyMjIwzZn6RlLfy1MIaM8EMwIJJw+2Eofaq62QwSRiH2Kat\npSXSxpc+caSHV0VpRJzGOnZ09nuaplqWY7j83af+jm/f+m2kI1Gp4soXXcnvvvN3EVJQtrX8ZXhs\nmDu/eyf33XsfYRyipEIaElLo6evh8osv5/XXvJ4lg0vocrqYrk1z0403kbopylRced2VpHbKpD+J\nH/l0W93U/Tp+5BOrmFCF1Ft1hseHmaxPMjE6QdAMdIJPAPjowtyDRGpjLkUolUr0dPewqHsRpb4S\nbsWFPJiOSc7M6SFfhu6OSnZJx6MGDVI/ZWp8igMPHGByZBJacOTpI3zxC19EoTg8clg/XwOkkqQy\nReYl56w7h63btpKr5MiZOaSpJxhX3AqVXIWSU0IKyTJjmR6sFvpUvSp3/eguvnv7dxkbH0OGEjPQ\nmn8Tkz967x/xX976XygXyhyZOULYCjGE0WkSXdOlv9gP6Maw5JQ6JvEFcygUnc9bKe0fKVgFlFLP\nqN0Pk1A3BeInF+pnWsiHof4CsG399Wwen5Hxi0bWFGRkZGQ8DzmT2MVTHdMu8hthg5lghljFpCrt\nzBmYnwLTKQbTkGl/uqMNz1t5bVoWFl1OF0oopv1pbGlTcAo0Wg3qUZ00TSk7Zb7yz1/hpi/fhLQk\n0pNc/bKrefe73k2iEvzQ5+jQUf75xn/m/gfuB6nNylJJUidl+cByXvryl3LB+RdgWia1qEZ/1E9k\nRHznB9+hFtVInZSBxQOs37yeOI5BQqISWmGLelDHNVyGjw6z+6ndDI0N6UJcsHCoWHvCsA+r1qxi\n6bql9A/2ky/n6S33dgy4U94UQRigpCKIAsI0JGflKJgFEOjV+sSm1+zFKBusWrqKSy+4lPtuu497\n77oXWnBo3yHtQ7CAln7eymCFzS/YzLpz11HMF5loTjDlT1FySp1V+1bcQqEIY/3ZCARjU2Pceuut\n3HbHbUzNTOndCQeUr5CJxJAGn//c57nyhVd2VvMtw8KP/I5/oGAVKDpFHEP7AxzT6ewQtFfslVJ4\nsXeS3r89vbh9zcyXErWvt/Z5cnYOU5oL5EEnxYmeptA/6doOIZinEmr/fLrjMzKeD2RNQUZGRsbz\njDOJXWwf0wgbnWSgnnxPx3jbvq1jHEbRaDR0PKfpULAL+v405HjjODOBzqrPk+88x0BxgCiJGPfG\nSdKkI8cJ07AjGfrW97/Fp//50xiRNvVef/X1/OVf/CV+4nP7Pbdzw0038Oj9j5IaWiKkEoWQgrPP\nOZvLr72c8zadp70BAuI47hich2eG+faPvk2SS8CETds2UfNrpKS4tosrXfYe2svO3Ts5sP8AfjLr\nCzCAPHPxpQrKbpnBswZZ1b+Ks1eejZCCWlAjjEIMw2CmpXdS2kPYDGlgYGBaJlJKep1eeoo9REnE\ncG0YiaTgFpicnGT34d0c2HOA6SPTnYIdBy1LAlZvWM0Ltr2AdWvWEauYJE1ISclZOZBzcq6232Ki\nMYFKFUfGj/DVW7/Kbd+9jWajSUqKyAtIQcV6hyWxE973rvdx5QuvpCfX0/m8LWlRcSta+mNGWEJP\nvG6GTSzDOskQ3DYPN6MmQRroCFohOnG0p8I27I7p2Y/9TqPRfg1RGnXMxZ1r9lkU+uHJIUOEYdYU\nZDy/yZqCjIyMjOcZzxS72C7m2klD85uHkfoIvflegJOKuRl/BoUCS9/Xils6cnRWFtI2xbaJkjkd\nuUKhUkWURniRh5QSFOzZu4eP/e3HwITUTrng4gv47f/nt7nltlu48V9uZM9je3Q0JhJsSETC+VvP\n57Wvfi2bzt1EkiSMeWPEia6gFYqckSNMQmbCGR5/+nFSR5tyF69djBd5jIyNcLwJRJQAACAASURB\nVPDpgwztHyJoBNoE7KAbAgGEUCgW6FrURc9AD2ctOosV3Sv08C3LoRW2SJKEVtQiItIyqchHCEEz\nbGJIg558DzkjR0JCkiZ0F7txLVdHmXohe/bv4eDeg0wfn9bPKejEieLon4tukde+9rUYrkGf3YeQ\ngpJVQqGo+3V63B5qcY28zOMaevaAJS2eGHqCHd/bwZ133kkQB6SGlk5JJSnJEo1Wg8RIUFKx8YKN\nXHT1RYw2RgEWeEIquQp5O0+Yhp3P3jT0gLJ6UF+wit+eFTBYHKQe1PUuRSJQqAVpQyeu+tuG3ZGj\nBYEibM3ebtk4tjjlLsFJ13VW6GdknDFZU5CRkZGRASzcQQjigGO1Y3p139ar+1ES0QgaFGytAY9U\n1Ikk9WKPsl0G6HgGZlozWNLCj309WViazAQzCMRJq7yWYeFFnk4dShWT45N85EMfIUR7A1auW8kF\nl1zA29/xdoaODqGEQrgClSqkLbnkiku46mVXsXbFWrrcLgZLg1iGRTQecaR2hCAOtJbekIw3xzly\n/AiBDMAEs2hy/yP3c/joYcKZUO8GxLNfs01BqVRi5Tkr2bJmC0sGl1AP6iih8GOfKIlIVUrZLWPn\n5vwUruHixR5+4neGjfXme/Eij0bQoCvfRS6Xw5vx2LFrB3v37OXY8LFONCoGOpFIgJt36VnSw/Dk\nMMSQ78kTEiJDSWRFODhMNafIWTmklJTsEjmVI4r1axsfGeemHTdx/8P3k0YpUkh9fgWLFy3mggsv\n4Aff+wEJCSIRLF66mJe84iU0oybjjXFSlVKySx0vAIlNHDnUAx8hbQaK/do4jt4RgFMYgikiohIi\nCSnNSnvCEJotfayTswmTU8h/EpsgnLezFYJj/t9V+ra9cFehfVtGxvOZrCnIyMjIeJ5hG3Znxbb9\n75JTWmAIbQ8RC+MQU5rEadxpDtr+AUtaKKE6ciHbtPXqv7QJU32uvJ1nsDjIcG1Y5/mb2kdQcSud\nYVPttKIojUiShJnGDB/5649Qq+ks/Vwxx0w0wyc+8wk9Y8BSKENhS5sLr7iQK66+gu6ebkzDxJAG\ncRrrqcNpTNkts0Ku6Jh3xxpjSCRTk1Odv4Cxitk3tG9ukFkIRFDuLrN141ZWb1zNiqUriJOYVtzS\nUazSJEUXykmaECcxCkWSJnqmgRQkaYIUkm6nG5R+3wyhZwy0pls88fgT7H1yL0cOH9HPm6JNywJQ\nYBZMNpy7gfUb17N0xVLufeheho8PgwRpS4TU8wBSUv2+CEXJKZEzc8TE9Bg9HDl0hJu/fTO7ntil\nk49cIAdpnLJ80XJede2rOH/T+bz/D99PMBMgpaS7p5vX/6fXM1AewDRMLX9KTWoipsupaPlOGFI0\neshjk6oQEmuhx+IEwhCUAkvaWFLPLyAEFYAKtU878Bb6ADpFe2LjGAsTgUhOruCfTaHfvv1M/AcZ\nGc8XsqYgIyMj4xk4E0Puzwvt32X+V5REhEa4ILoxTEJsadOV62LGn5mbNCttXfzPvgcFs4AlLRaX\nFtOb7+00Gs2wSZREVHIVoiTCCz09pEzFuMJlpjXDqDHa2S2wpIVSipyZo+gU+YfP/APHho6BBBEI\nglpAq9VC2YpEJBRzRS57yWW84qWvoC7rRGFELajR4/booj0OMYVJLaihUHiRR5zGBHHArid3sWvX\nLp589EldgDt0VsxJwMgZrF2zlgs3XciGVRuQlqTeqjPZmsQSFqZpYiubieYEhmlQskr4kY8SiiiO\nKLpFgiQgb+W1bEmBUILpmWlGjo0wdWSKI0eOEAaz1Wio71eR6uxanLPyHLZv3c6GjRvoynVxvHmc\n6WCa/Qf3d7wEyxYto2SXyFt5ClaBKI60JEklWIbFgb0HuP3W23li19Mo20AaXYgU0jBm1aqlvOjF\nF7Nl4xaKosiH/t8PMXZoDGEI8l153vue92KYOlkoiiCJTFoNn0XFIlbOplpFR8bmI8y0RKMRUJ8O\n6O2DQoEF10iHxD6paZiaAnNeFdJs6sK8p2f2rQn1bQCWZVP8CVX7mRb6883I82+bf46MjOcjWVOQ\nkZGRcRrOxJD7XOKZGpgTpUFBEnTiIQHqQZ2SU1pwvh63R0tGkqhj7GxrwG3DJm/m8WKPaX8ahaIZ\nNfFCTz9Y6ef0Io+RxgixmtP1h3HI8eZxnVc/u1tQbVUZb4xz247b2PHIDmROQgSWsEjTFIWiXC5z\n2Usu45orr6GQL+BaLn7TJxABjuEQq5iaXyNv5xlrjDHcGCZOY8JWyBO7n+Cu++7i+MTxTmHd+QsY\ngnAEW1+4lf7F/QzkBljes5wgDZiuTtOMmoRpiETiGi55O08tqmGnNt1uNyWnpJOQYh/X1N6A6kyV\niaMTHDx2kKGjQ1r+ItBL4m1ZUgoYejLv+jXrWb9lPUtWLmFF3wpcw0WhOFo/yrQ3zcTUBMdHj3ce\ne+HZl+O1EiYbEFoGhinoK5Y5sGc/d3z7nxg6OIrwTOxGhaQQkRRg89bNvOyXrmH12uWEqoGKYz76\n4Y9y8PGjyCiPoyz+1x//T7Zt2cRjT+1lxmsRt3JYFBAixlIFwhBEapOTJVTg4DWLhC2bxBBEYUhs\n2PTli1oaNO9aFIbNiYOGowjSVH8HXZi3hxS3TcPt++p1KBb1FzzzDsAzFfbzzcjtnx1n4S5D1hhk\nPF/JmoKMjIyM0/BMhtznGj+pgTnxd4mSqCNpmY8QgiiNCGM9ebgn19OR+ABEadR5TDNq0oybJGlC\n1a/SjJpUchUsw6LqVTneOE6k9E6BEookSfBCj0AE5FQOU5j4ic9Ma4bjteP823f/je9+77sA2gBs\naGnPWZWzeNnrXsbFl11MI22QM3PkrByWtOhz+0iTFCklQRyQt/P4sU8jaDA5Psnt99zOnqf2ENdj\nXYQr9F++GPp7+zleOw4JqIbi4QcfZt2562AQBkoDWgZFSJAE2igcNWlGTT0xOdEm4rHjY0xOTzI9\nOc3Y5BjelMdEcwLf87UMyULvREj087tAAOVcmZUrVrJh7Qa2bNxCX7kPP/FJ0oSiXcSLImpek6an\ncEUvD91/u24I6nDuOdtYVdlKw/epNQNq9QYP793Fk7sfY2ZsBhHHiNjENAoUir284KKreOHVL6TS\n240yQlrNiIIs8dmPf4TDO48jo0GksnnPu3+XF1/4CqQR0W1USRIfO+1FBHl6y0XylkUcgSVsmnWb\nyLeJWqCUjZQ9RLMegTAHxdy8ay+cK8DnF+2WBa3W3LWnFJ3G4cRoUaX0roHj6Mbgpy3c5+8QtH+e\nb0bOjMkZz2eypiAjIyPjF4Bn08Cc7rZ23GS3291JIBprjmFJa8HE2faxtbBGmIZ6hyDREh0v9LAM\nSycRAUmSYBomsYrJ2TniJKYW1rCkRWiGJHHCj3b8iC988wtMTk/qF2MBESxZtITXv/L1XH/l9XiR\nbiwqooJjOCRpoicMmzZ9uT782GcmmSGIAx545AHuve9eDh06pKcJgy7GI3Bch3POPoft529n9bLV\n7H54Nzd946bO0LF9D+xjn9jH7eJ2KpUKXYUupCNJSPDxSUkxAoOG16BerXdkR8i5141Bp/FoY9kF\nlvetpe+sQXp7u1mxZDHrl6ykaBXxWiFpUEL53djCIoljbMOHNMRRXRw5PMzRJ6dxqGBa8KqXvI2y\nWMrM+Bj33/E0ux7fQ8uoIlIHJ1mOTFJyhuLiS1/MK6/5FYq55Qgz5OjUGH7SxCzFfOwfPsbuh/aj\ngj6klLzrPb/LdS99HVZcxBU2g04N005YWVoDea3hL8iQWIVYDkRNmyix8TxdsOdy0GhoOVB7Vb9t\nJA4C3QAoNbcb0F71n79TUCyC6+r721/WbMiVbc+t6GdFe0bG/z9kTUFGRkbGaZg/rGv+bc912jKi\ntoH3xMFjtmF3hkvNPyZKowXnsaRFlERY0uoMkYK5dJkojTqGZJUujBsFnSgUpRFlp0wrahGrGD/y\nyRk5DMPg+z/6Pl/68pc4euwosRHr6cMKUlJe/ksv542vfCOVQgUv1pKkvJXHNV3iNCYmptvpJlZx\nxxD91INPcdu3bmM4GNbNgIOW7BhQ7imzbcM21q5bS2+xl65cF67l8oqXvIIli5Zww9dvYLI2qeU9\nQJJLmJyZZHJyUp/DZIHciJDZHQADAhtyAQi9u0EC0rRZsvIszl6+nmXLV5BaFmbSSxSHCBnjRP3E\njTKiWCBpJlj0URZ6OFezESNkBE0TI/S467ZvYHndkKScd+52aqOKv73hCxx+soqKi2CUEWYfaRpg\nK5fLLrmIa6+9lN7iSkpOEdOAKIR+2UMjHuVTH/lr9u55EhH1IJIC73nPu3j99b+CJSFqQasJXt0l\nZygKVhEsXbhLpXX9hQKICEyhb6/VII51M1CpzL5F4VxT0KZd0Asx93OptHBHYP5OwOQkCyRH/x7N\nwIkyofbuxb/nc2Rk/LySNQUZGRkZp+FE6c1z2Wg8f/rr/OL/dDKi9ryBBbrvRHQGllVbVUxpIhC6\nUA2bRGnUSQ1q6+zbOwIAfuxjz0ZFpmnaMRHPBDP4sY8lLQaKAzyx5wk+8OUPsG//PkQsQIGUkjRO\nkabkumuv4/WvfD19RZ2/HycxURoRtSI800MgyBk56mGdkeoIO3bs4I4f3EHjuM7Qp0xn4vCaNWtY\nu2EtixcvpsvtIlYxhmFgmiZ5M48tbbZu3Erv4l727N3Dnr172HdoH4Ef6JV+Y/aXU+jpwQkQSW2c\ntRzybi8DPctxBhLKiwy6K3kGu88iZ/fQaMUssc7BtBSjM1PYRg4jAVuYOMqhNm0x4PSTiwqIxgCY\n4NOkXheYpoLY5I7vfYmp4RgoI+M8T9+d8sjN3yE28qTSIE0FKIslAyvYtu0SVq5Zh2XlCWpF6kER\n34SBAV38xkGdv/urj3PgwF4MQ5CmBd75znfyutf9SkeeIwR0d7c/US0Za6/sl0pzUp5CQe8KhCH4\nvj46l5tb2T+ja3b2XKcqytsNQ70+9+9/j12C+TIhx9Ff8+/LmoKM5zNZU5CRkZHxDPxHNwI/bdrR\nfJ3//NX/9jnn7xic6nHt4+phnTAOSdOUINVa+pRUr/4LaESNTrGfN/NEKqIe1BEIBouDdLvdNKIG\ntVYNBHiRRz3UMpunDjzFN275Bo/sfAQlVWdImYVF1IoQpmBReRG/9spfI+/kaUUt/NgnVSmR0h4I\nK7GIk5hDM4e47c7buGvHXTT8BqRgYCBiQckoceElF7Jh0wZkXhLHMXmrjEoktlDkjAqEBRqBxMGg\n5A7QbYWsWgkDS1fwcqOI5zeZrh3Hq3t4QQCJRcUtIo2EVNlI+rDlEkTUR9kuUTeGCOwRbCciaBSo\nTlu4okyhuAEiRZfvoUyfOBIIZUCYo7+/m3xaIA4dWq0SzaZNHCyG2MbtCvnObTdy390jYPdDS0Kj\ni2arC9McJsEGu8ymzRu48PxLWLFkMzNTDoYCS0GrBkZBJ/mYJjSbw3zgA+9neHgIIWoAvPOd/5l3\nvOONBAFUqzoNKJ+HRYsgn0/xPImUuklor+KDbjCKRV3QOw4MDurnKBQWFvXt76eLCf1JaUHzJUin\nuv+nJSv+MzJOTdYUZGRkZDxH+L9NO2rLgtSJMS9n+NyNsKG1+0p1Jg37sa9X6pOIvJNHIBBKYJkW\npjQpm2XyVh5DGtimrQeXpfqxlrS06fh4lS9+9Ys8cO8DnWFjAHZqc/WVV3PnjjtJWgnCF/zJH/4J\n5y8/n2bY5ED1AKZhMuaNkUR6ym51ssoPfvAD7nnkHuJGjDJUJ1K00r2Mq1/yas7buplKd4GAKpPN\nUSKZUjG6Se2Ihu/RagpsK4/CpTaZJzAs7NwAi3M5ptMGaWqQz8Usr6xH4hD4CbYoolKLqCURhmJi\nzKI1VaERhzRkiLBXoGyJLEXEkyXsZIA+cwX16SI1PyJnWygFpgTDiIi9gLpwUPUCRtiD37CJYy3D\nqVYn+fKXv8bOnQ8B69pXAxAghKRcXsMll1zO1q2XsGRJL4ah9fxpSRfg7ZhPx9Ea/cOH9/HBD75P\nS6HwkVLyR3/0P3jLW15PoQBHjujVftPUq/3tIr6rK6VYPLWWv635l3KhV8Cy5nYU4CcX/j+pQM8K\n+IyM/ziypiAjIyPjOcK/R9rRT+ODaDcjnYFkSmGbNmEcUgt0xGclV9FTjGd9B7a0KZgFLetJI1zb\n1ebh2RjSglVgYmKCz3z+M3znzu/QMlpIJMpVqFhxxSVX8KpXvYqdO3fSTJtgw6oVq7jkJZfghZ4+\nb6iQymGmKjhwdIgH7rmXx3buIjUbKFchlUTEgkWLFnPpRddx5bbXIA0LQ5jgmRhWHjPn0ooiilaO\nSLSQRhcy6cZqlXFMCzPsxw8imolFYklU0oXrSJKoxtFDPo16HTPqplgx6CpVaE5XMIWNd9zGiEsU\nUmjEU7hdHnajn+S4jdkokvjdTKkehIAkAWPW8Ow44BagtwhuAlYEpgW1KOGRR55k586dPPXUo0CA\nlAlpmgAJQgSsW7eWq666jOXLt2PbFoWCNupKqYv5YlHLbcJQF+e2DUeO7OKv/uo9+P4+DMPEdV0+\n+tGP8opX/JL+7Gcvud7ehdGgnifp60vp6Tm5KJ9fqBeLJ+8EzJfknHh8RkbGc5esKcjIyMj4BeKn\n8UHMP9YyLMJYDzUTCPoKffocs1OK4yjGNm0c08GSFl7sMZgbJFVp51xjk2P869f+lZtvuhk/9Eny\nCUIKELBt4zZe/crXsXbZRhIVcfc9n0PRD7i8+MVvYKxap6tkUfU8VJjjvl1P8m/fv42nh57CUAor\nWYQyKogkYNny5Vx1xZWcc+5GwnqZs3u20fKhGbQYm25SDSQ9fXnKtqRiWQRxSJdtYbOIONDxmp6n\nOHxgP7v3PsXkzAgzkxOMTRzGa81AOABhPyQlUCZdlW6Wr1jDC7ZvoGivoNW0tS691YMRam9zHINM\nYHwcjrd0MZzL6ULZmPUnzE/YqdWOsWPH/dx996NMTdURoo6OMqqgVIKUPpdffinXXHMZfX3LMU2Y\nmdGNQD6vi/9cTj8vaKlPOwVoz567+MQn/idxXEWIPF1dBp/97D+yfftFHSNwFM29lkJBnyOO22Zg\n9ROL+WwycEbGLw5ZU5CRkZHxHOHZrvKfzn9wqkZg/hTj+cfMbyLCJNTyICGQQmKZ1oLVf8uwGCwO\nUrSLNMIGkYqwDIu8lWfan2aiNsG/fu1f+dLnv0StVkNZSp8vFGw+ezO//IZfZvv6C6k3IyaqLUYO\ntzjyeBcGBdx8i40bz2d8IqbiVhjaf4Av/svXeGDXIRJHYRk9ECmMyGLFylVc9eILWbZ6FVIISuSQ\n8izscJBWK8Sv1TEih1xUxPEUduAgU5BxiGvZPLl7nEcf3s+TTz7FkweHaIURKFe/UUYTqEDUA9FZ\nkPSis0bzzHgxu4fHeGrnKG960yBB0IvnzcllcjldsPu+Xj1vD99SCvr79emTBKanJ7j33kd44on7\nOHhwN0JUUKoEpAjhof80V8nn4c/+7H0MDKxCCN1MyNmpwHGsG4JiURfwvb26IfA8/Rq+971b+Nu/\n/RuUMhGiRF/fWXziEx9i5cr1TE3NmXyTRDcX7dX+dpPQ1ZWccXE/vxEIQ92UnHh7RkbGc5+sKcjI\nyMh4jvBsVvmfjf+gfez8xyilOt6DMAmZ8qcAHSNasAqU3TJhCCMzEzqW1AbTUHOvKbEhLBDFHg0/\n5L67d/LXf/M3jAyNQBgjDIFMJWdvOJdfe/vbOe+8rfihT9C08BsRZlDkiUcexkiWgBGybLAfK13O\n2KEm3/j8/8f3dnyDmALIfkSQw0gF69at58qLL2fl6mUYVkSXaRP4NjKyyNHH+DjEsY0KSwSNkCSA\neghSKvZN7OfhB/ew84EHGR31gBI6XcdGr/HrQQZCJChlUyiUKHb1o1SZMHSpVgGaQEAQJNx6621c\ne+3rME0b19Wr7O2CXUq9Um/bupC3bZiammRk5GkOHtzF0aPjwDgwgxCL0bFGDUxTkSQCKVvYdpP3\nvOfPOeusVcBcKpAQuvlQSjcGtq2fu7u7nf2v+MpXPszHPvYllOpCqTJLlpzFBz7wP8jlFtNo6GYi\nDOeMwoVCWzKk7+vuhokJQRg+O2/K/GnBkE0Izsj4eSNrCjIyMjKeQ5yp3GfKn+oYgtu3NaMmRbt4\n0rnaTcZ8z0LbqzA61aBaDwgTC9OJsKwIL3KoxRDEIYkJwoAwAFtC6ISd4k8Iwfj4BB/7xCe55/4f\nowyF6/QhTJuli/r5zXf8Oi+58qUIIZlq1innFlGvgxc0iQNBbSxGxBZxatLbfQ63fvUH3HfPj/C9\ncQK/mzRdgjINNm26gGtfehWmPAtpQDRtY7ggzJDEB9cqknOLRKlepQ4CGxHa1CeH2LnzYR588AAT\nE4eABjqvtAfdECggpKenj3XrVrBkySqWLu2lu7sXw+ihXBZMTmop0PS0z9Gjh7j33rsBwejoJIYx\nRV/fILPDnpma0pn9U7q/olCYZHz8CE8/PcTExCgwCkyis05bQA7LkqxYsYHR0WM0GmNICYbh8a53\n/S7bt28A9Gp+2wTcLuDbK/r5vC66i0VIkhne977f49Zbv49Sq0jTbtau3chv//bvYdu9TE3pc9i2\n3s0ol+diOdtf7aShiQkIgmffFJzqtqwpyMj4+SBrCjIyMjJ+jgiTkHpQpxE09Gq/UHOa/ySkFaTE\nLQeR2phWQG8Xnaz9MIRw1kxq29CIYLoWohQYwiZs2kSzCTIihGYUYlo2+bytC7t0NqXIh3o14R/+\n4Wa+9s1bSBKJ45QRooXjdPMrb3wbr77ul8kXQaQCkdp0Ow5+EBIHUFJ90LLxJhK8KQUU+fF3niDy\nE4TKodJFIFw2bNjKK667nqWDG5iZ0a+/VIKyBXEA1WOzufgFmGy2C2SPBx+8jx/+8B727RuZ3RHp\nBgaAPsDAcQqsWrWcLVsG2LRpDWk62EntcWdVREmiV9LbUZ2WlWPz5g089NDts4bchL4+qyPjaTa1\nyTdJphgZGWVoaJipqaeBqn7jSIEGQqQYRouNG89j69YLGBws8elPfw7Pm0SIHLad8Ad/8B4uuOAK\nlNLFf1eXfi0TE3p3wDB0Q1Auzw0De+yxJ3n3u/+QoaFhYBlpWuK8817E7/zOb2EYBVotfY5aTQ8Y\naw8QaxuS236C+QW8bG99ZGRkPC/ImoKMjIyMfyd+2hkDz4ZG2CCIA0zDJIxDPVTMiLBdm6YH0xPQ\naoV0uTa5PKBCeis2YRygEhulZuVDsU21AZa09UTgNCCKoNXS+nRL2ljSJoxaHZmKfpzFt771PT7w\ngb/iyPAoypIoaRCHklde9yp+5U1vJ+/2EYU2sWdjuzaeN7vSrWBZNxyPYfhQwNF9HtT74P+wd+Zx\ndlRl+v+eqlt193t77ySdDbKQQBJAIhC2sG8qRjYFFBVFUcQZcRQXBgZmxnEc11EGkUUQkEUEUWSL\ngAQDSQgJMZCQPWnSnd63u9e9Vef3x+nTdbsJ+/IDrOfzuZ+7VZ2qW3WSfp73PO/70kIZF+hH0suU\nKYdw5JHHc8ABM0Yq4nieb9HREenubvWsqvDsYsWKxfz1r48wMKASdZUQMAEL00yyxx5TmT17D6ZO\n3YNYLEwopEiyHrOmxrf7ZDJqfNNU5NswoLd3J+XyiyjLUS2uW4NhgBCDPPvsDjZt2srOndtQHc6i\nw8cuABkgwYwZEznwwHlMmTKXaLSeHTue5+c//w2FQhHTtInHTS6//Iscfvh88nn/d/b2qmdtGwJF\n5nUJ08WL/8R//McvKBRMhGhASsmHPvQxTjjhXEzTGhE4dXVKDAwOji4Zms3uPqJvWa9vpeCVehIE\nCBDg3Y9AFAQIECDAW4A322Pg9RwHVElQQqo5WL5YwS6H6e0SZHOSUEiRMynBEFCftBEehE2BGO5S\na5s2hYpNLBrGcUvDZFNZgygmkdiEvATSkCAdBNDT0cbF//ZDHl38JFLWIDCgaDN95n58/osXMHny\nbLySTT5vK9JqQ27Ib0AVjUIq5fLEEw9xzTV309YWBcJAO5CgoWE8xxxzNlOnzsXzBJ2dSkzo35NM\nqgj+4KCKoFuWZOvWTSxbtoyNG9fgeTkMI49hRJEyhxBJ9txzLvvsM5MpU/bE86IjlX9ME/r7/Q68\npZJaachkVDMu11Uee117X0rJsmUrgVqgl+nTa1m37ml27FjH5s1rcZx6lAjwUNWD1OspUyYzf/40\n5s+fi5T1IzX9n3nmWe677zYqlQJChEkmw3z/+19in32UZailBdra9O9U1h7XVbafaJRhMVLiZz/7\nEb///e/xvHogRiRicv75F7HPPoepSkiGuv6hkBI9lqWew2F/hQP866BFmBIcYiQpeWzSsOO8tOJQ\nUIkoQID3NgJRECBAgABvAd6KHgOvBbZpM1QqDpM3G8urwTIFVGxcB0rFEmbYxjOg5IBVsIerwdjY\nlk0i6o8lUmploFyEQklQzoVJhWxC0kYC+YzNUC6JNAb4wx9u4o7fXkulVMHzYkiZJBZrYNGiUzn/\n/E9SLJqsWaNIZTyuiKf21k+cqD5fufI5brzxGnbsWIWUMUxzHGAiZZT6+kl88pMXEg6HMAxllXEc\nFaVPJtWYxaIax3VLrFjxd9aufZT29i4gjqoQlEDKCpMmhTnssBNpaTkEKeuxLGXByWR8gp3PK8Jc\nU6MEQiajztUwlGWou1sRcF2lZ8WKp2lra0PlA5TYvHk5W7Y8gmE4eF5y+PMaIMyECePYa6+92H//\nvWhpqadUUuP09yuyvXr1Uh566IHhfUwaG0NcccWXOeywPUaSfW3bJ/Q6ubi3V/3+ZBJ27drFlVde\nwYYNqxEihBAVJk5s4qKLvkFzs0pOjsXU8aJRVf0oFhtddjSXU7+ttlZ9pgVLPK5fixHbkV6h0ALv\n5RKKAyEQIMB7F4EoCBAgQID3EGwS4KroPQClJIlomFwZhBfGkmEqJRBhS9MedgAAIABJREFUEK4N\nFXuEtBWLo7vN6sZT+YxNuWwTHybPuZwixLmcZOmSp/nNb/6Pzs5dGEYS1xWYZpoPfehkjj76BCKR\nOMWiST6vSGc2q/a1LDV2TQ309vZy443X8eijTyNlCMMQSBklGm0gn3fwvBDd3X288MILzJkzZ8QW\nY1nq4bp6pcDlmWdW89hjf6W/3wPKqIThMuAxa9Y0jjrqUA4+eA6eZ9DTo4h4oeBXBqqpUQS3s1Nd\nj6EhnxT39qprEouBYfTS2trJ9u097Nixk2KxgkpMLiBEASGKCJFFCBchbPbYYyL77XcgEyYcSCTS\nSDjs9w+oVNRxCoUSTzzxF1ateholCEKMH5/m+9//PNOnN40Q6nLZX1kJh9X7SkUJpEoFVqxYzn/9\n138xNFQConiexxFHHMfnP38RlUoC01S/N5XSvwWamtRv1MRf24aq34O69/E4lMtiZM7p76ufxyJI\nKA4Q4L2PQBQECBAgwFuAsT0GHAeEZ5N13nz0tNqq4Tg2STup8hccKJdtunptVQ/fgVhIRZbDAkRI\nETxtDXEcRfrCYZ8UajuJlOq7clkR461bt/GDH9zI6tXPASBlDVKGmTlzLl/72nnMmjWLzZu3UixK\nWlt9IqvJPEA0Wuahhx7m3nv/SD7vACZClIhGa/noR09j/vyPcf31v2fNmnUAPPzwA0QiFnvttRfj\nxqmxVO19ybPPbmL58sV0d3egPPtJoB7TrGXOnKkceeS+tLRMJBLxrVO6m7CUynIUjarchEpFiSPT\nVK/7+8HzsuRyrWzcuIGtW59n+/YeVJWiGpRlqAj0oJKFBfG4wZw5BzJr1r7sv/9c9txzMpmMqlQE\n6hp3danVklgMHKeXO+/8A7t2dQLqZk6b1sTFF3+WVKpuRETpJmSlkn8/0mlF7A3D5YYbbuW2224D\nBFJWCIUinHvuhZx44oexbUF/v/rNtbX+vU8mYfx4dS7akqWTjHUys56f+t6B+jyX85OSx3YqDhAg\nwPsLgSgIECBAgLcAo5qAOYBrY5k2Uu6+XvvuPNm7g+Moa4svCiCRsLFRY3tlZXeJRBTZ0/ad5mb1\n2vMUsau2pCQS/ljVsCzo6Mhxyy23cscdj+E4AsOo4LppksnxLFp0MieeeDgtLcYw2fTIZg3yeUZ+\np2mqx+bN67jzzutpbe1H/amxMAzBkUfux/nnf5G6unHkcnDOOafS1tZFT08O1+3gnnt+w9y58zj9\n9KOJRhvZvHkTf/7z0mHrjg2kAYhEYsybtx+TJ88mFKoZqcijk4SLRfWbdD3/YlELIUm53Edvby87\nd/awZUuGrq4s3d3bgE0YRgEhBEp0VIA8SoQUCIUsjjjiOI48ciazZu2JYYRGch4qFejoUMfTFp1I\nRF2LrVvXceedt5LJVFA2pxwHHDCXz33uLBob4yOrAdq6Y1lKsEWj6nW5rPoc/OAH32PFinVIaSFE\nkaamBv75n/+VqVP3xnXVMWtr1X41Nf556PutbV26pGki4V8ffe1SKW0VkpTLBlL6qz5aFAQJxQEC\nvD8RiIIAAQIEeIugKw5lHZDm6O+q7RWvp8mTqrvvv5dSfaa3rVRUJFpHxhsbfatMqaREQbXlI5Wq\nOl9bkWXLgr4+ye9/v4Trr/8dPT07kTIBGHheDUcddTwnn3wyyWQtkYiKuicSkM0alEoGkycrD74Q\nkMv1ct99v+OZZx5CCIlhOEAtLS3j+NznvsTBB++PZSmrTigE48en+ad/Op0f/OA3ZDKqbOfatWtZ\nu3YxyWSMTCaOyhlQrNeyEsyadTDz5s2mpiY1EulvbVW/pbZWkfFEosTOnVm2bu2ns3OAnp4hBgYG\n6O8foFxWEX9lPWoYvhpDQA2eVwdUhpuYRYAY4BAKwWc+82kmTpzI1KmK7HseI7X/TVOR7khERebL\nZSiXPR555F7uvfceIIEQBqaZ45OfPIMPf/gkQiFBNOpXFdJ2KVA5D/rebt/+PP/6r9+mvb0PsJAy\nydy5+/HP//xPRKP1gF9O1XXVvVErC+qcEgn//mvLGPhCQD/rh+OoHhS2LUdWlarnZpBQHCDA+xOB\nKAgQIECAdxiv5Ml2nNElIrNZRfg0bNuP7Or3oZAmcv5YUipC2dWlCLi2CU2Y4I+lI8WbN7fx7W//\nnOXL2wAXIUwgy6xZe7No0flMn74HpumvNhQKDIsDA9eVWBZEow6PP76Y++//E8XidkKhLiBJLGZw\nzjmfZOHC0+nosFm7VpFgIdTxVUnQaVx88Vf505/u59lnl+F5AwgB2ezg8JmWgAS2LamrizA01MlT\nT/USCnlIaVAoxCgUwPPyuO4g+XwHlcou1KpCM6rCUREV+U8D44Bu1MqDhbLzhGhubmbq1KkIEWLZ\nshUoQQC2HeOUU86kpWUitbX+9XYcP2cB1O/SxBx6ufnmX/D0088CaaT0aGiQfOc7X2fGjDkjKxfV\neQc6su/PCclDD/2Ja665mlKpjGGohOKzzz6es8/+LNmsOZJ7YJpKiBiGuu/jxvnzST/0/dNzRIjR\nOSbVcywWk0Sjqozp7hAIgQAB3n8IREGAAAECvIXQBExXnKmuB/9a9s1kGJUY7Dh+tF8jkRgd8dWV\nc4RQxFB3pdWJpLobbnVSqeNAsehxyy23cuWV15HN2ghRi5QRUql6zjrrdE455QhKJTFig7FtZZHJ\nZnU3XJd83uSBB9bzpz/dya5dOxEii2m6eF4TxxyzgAsu+BKpVBM9Per4ruuLIi00MhlFikOhJACG\nUUBF53uoVEp4XjNg4TgFOjraUVH98ShrDyhyb6BWE1SnYJUPUBl+hIcfAsgBJg0N40mlGmlsTNLc\nnGbSpCZMs5Zly5ayfPl9qDyCMvF4Pcce+yGam5vJZnXnYHXUwUEV0Y9E1PUFRey3b9/Af//3lbS3\nv4hpKs/N7NnT+MIXvsy4cfW4rr99f7/aPx7371FvL7S393HDDdeydOl9SBkFYiQSZS655FIOPvhQ\nikU1P7RVKjVcSaqxURH9cNi/ztr+o8uL6vmjVwF2h1DIo1w2/InpONhhwAmWCQIEeL8iEAUBAgQI\n8BZBiwGdwKmjsdURW82hdufJHp1QrC0o6n21n1tvWy6rz4UYbU+Kx/2k4uqOtVooADz//A6+853v\nsGzZE7juRCCMECWOPnoRH/nIh0in01iWTvT1yerAgF+hqKfH4e67H+CZZ9pQpLwWKctMnTqD8877\nIgceuC+FArS3q3NtbFTENJtVxFpFtsusWLGUpUuXUCqVUeS9hGkW2XPPacTj4+juLtLZ2U+pFBv+\n3sMXBAagK+WEgAiqSpBBOu2RTNpEo83U1taRTtfS0lJDfX0tsZgxYs1S1Xb6uPfeO9i2bcfwGLWk\n03GOOuokUqn6kd4GlQps365IteepqHyppAh9KCS5996/8Nvf/jflcg+hkIGUkkWLzuGEEz5JoRAa\nuZ86oq9LfZbLfmOxJUtW8n//93N6e7sRIoLnGUyfPpkrr/wWEyZMHKkOpcXfwIC6383N0NDg32vw\n56G2J+nchVcSBKB6QEjpIsoOOCU17yx8BaonYSajBkskAnEQIMB7HIEoCBAgQIC3CNW2oOrykpr4\ngZ+wqewx6jPNp6oFgX5dHd3X25VKowO1dXWjrUfVOQe61r7edmjI41e/uo2f/vRnFIsZhLDxvBAT\nJ07nggsuYuLE2ZRKvpgBRSbzwxw8nVYrD488soKbb76TwcHCcFlOSSwW4aMfPZOTTjoO07TYvl2N\nE4moB6hodmenIti9vdu5//6/0NGxDV1WFGJMm9bCF75wEnPm7E25rPIV8nmPrVv72bWrj2KxQrns\n4roe+bxNoWAAFrGYTTptEw6bNDTUUFdn4Hm+fUonY+tuwOGwIuedndv4wx8eYWhoENV9OML48Y0c\nfvhCDKOeSsXPo9BJwLGY+i06wXnLll7uv/9O1qxZRigkkLKBeDzFxRf/C7NmHUKhoI6ZyajyoKr5\nGMPWK3UuQ0M5fvGLa7n33vsBCyEqQJmTTz6Rr371y4RCUYRQ+6bTam7oFRwh1Bia7FfPxeq5ovn7\nqC9fJuofDgsStqMcVrub5FrZaiWsxwgQIMB7EoEoCBAgQIA3gbHR/eqSji/3mS4Fqj3kmvjr7QcG\nRucR6Io0mpB6nv9dJqOi9jo/QI/R369ep9M+T3v++a1cfvm/DZcZtYc77EY45ZRzOf30M1ERckVS\nYzF/rFhMkWKAoaFerrvuVpYtW4uq0BNBiB4OOWQBp556BjU19SONwUzTr8wDSlgMDsLAQIHVq5ez\nevVTqHwBAXi0tDSycOFHOPjg2bS0CMpltc/QEHieQW1tPaapmpFpAaTLjXqeX2rTttVnUqrropuf\njV1JcV3J8uVP8PDDd1Mq6eZjggULDuDAA48nmw0hpSL/Og9CW308T92PwUFYt249Dz74KIXCdsDG\ndScwadKefOlLn2Py5Ilks361n1hMPfTKQGOjOu/Vq9dwxRX/w86d7QhRRsoK6XQzX/ziVzniiAWE\nw+q48bhq7KavgWX5ieV6PmWzw+TfdihlHUQOZEhdmFGcfWzGeyYzogaNSuWVCf6rJca8EXvRG90v\nQIAAbwkCURAgQIAAbxBjOdXYRlCwe17zSkIhkVAkX1uPqkk9qO+iUV885HL+6oGua68JvSaJ/f0e\nt912JzfccA2O0wPYeF6UiRPncc45FzF37nSiUbWq4Lp+bf98XvFEKdV3y5cv5ZZb7mRwMDPcgKyP\ndDrNpz/9Txx66AewLEWg+/sVUdUrBJ6nft/QEGzYsIXFix+ir68HleCbxTSbWbjweObNOwTLClEo\nqNUBnSito+OGoc6lv199p8VHLOYLJZ2zoLfP5xVBdl1/VSUaha6uXm666df8/e+bMAwP0ywRDoc4\n7bTPMnfuviN5DtqbrxOCLUtZdAYHwXEGeeCBxaxatR5IoCoZxTjwwHmcfPLJmGaUF19UY4RCSgDo\n1QbdXbimxuGnP72Ka6+9Fdc1AAMhBAsXHsJXv/pNDKN+RJDk8+pZlx3VCemWpT7X5VGlBFlyQJYI\n21B2wMlmsMoCW9iAjZ3czXKCXiKyLITjIPUErp7kY0sPjZ3kr6e01th/FG9kvwABArxlCERBgAAB\nArxBjA2WartQdWWgcNjnNzoQWi6PrgSjv9OfNTcPdxrOvzRRuTpKXt2RtqvLH0N735X1ZidXXnkZ\nzzzzDFJG8LwUphnhjDM+wzHHnInKJWAkcdXzFHHVUX2VCNzHDTf8jhUrVo5YhQwjx2GHzeHss0+l\npWWfEaJbKCix0t+vCGpzs3ouFIo89thdPPDAkyg/ShwoMWPGVA499OM0No4nn1eEV0rYsQPq69Xv\nqq9X56aTqHWTL9f1r3U+r95HIr5/XvdM0Im2KrovWbHiMW6++UYymTKmaQEukyZN5VOfupCGhgkj\nqzRDQ+o4jY2MdAnWJT+ff/45fvOb++jvz6KSmUskElM4+eRFTJq0Fx0dqtpTXZ2fnKwbp8Xjaswd\nO17g/PP/hXXr1gAOphkmFmvhK1+5lGOPPRnbFljW6JUhKdU10GVHczlIRRyMikPIAEvY2GEb23GG\ne1pAXcLBkSWcigAsbEq8hGrvJvIvqrua6e+TKhl8JCFjbDb97jxw5fLLlzF6heMHbZIDBHhnEYiC\nAAEC/EPgnXImVCcCa04jhEqw1c2p4vHRgVC9rZSjz626qRT4uQJ9fbBrl3qORNRn+bx6pFVvL4SQ\n/OEP93HNNVeTz/cBIaQsM336PL72tW8wZcpMent9i4+OjIPifb296ngbNz7LzTffSl9fCbAxjD6a\nmmr4yle+TENDA/G4pKFBEdRQyK/Go0tdZjKwceN27rjjdjo7tyFEBc8LY9tpTjhhIYccciBDQ8aw\ncFAEWFVGUqKkVPKTrSMRJTxmzPB/O/h2GtdlhETr1QFtrzEMyGQ6ufXWO3jhhWUYRhbXTSCExfHH\nn8KHP/xhDCOC66pz19fddX0xEIupMqn33HMbjzzyV6SsRQgLKSX77nsYBx10PIlE7ag8DlDXxTTV\n/pMmAThcffX1XH/9D/C8nhH7zyGHHMbll/+MpqaJo7pQV18DPT90BSG9gRaIyo41Bo6j9gujFjT0\nwK+1C9nu/sHU1e3+H1V1+S0NnQQTEPwAAd7VCERBgAAB3vd4u5wJ1ZxKcyGdPKrH1sVaLMu3dgAj\nXWx1pFsLhepzSybVmLpij22rcfr7FflWybp+A7JIRO3f2trLL3/5c5YtW44QhWFSbPDZz36Gc889\nn0rFHrGaDA2pccBvgNXbC21tWe6660GWL1+KqvRjYhhwzDFH8PnPn4Ntp+jt3YrqfKtLivq/LZ9X\nKwarVi3lD3+4A8cZABJ4nmDvvWdyxhlnUl9fPyJGKhVt61FjqOZoSii0tqoxJ0xQ29TW+mQ9k1HX\n0TT96ju6d4C/uuDy3HNLePzxBykUPFSTMpPm5kl84hOfIZ2eyc6dav9kUo1fLKpIf6Xi5w9s3fp3\n7rrravr6NhAKmbiuQzI5ntNP/zSzZx/I4KB/XuXhTtPxuG9xSqVgw4b1XHHFFWzatAkh4hjGIJGI\nwRVXXMGnPvUlhDBGJpPjQH/ZJpGwR0TBS4r8DJPyUfx+mIDbYwXC7hoS6O3DYV+VDkOO9bjt7h/A\n7sbUk776s1cTBa9VoAQIEOBtQyAKAgQI8L7H2+VMqI7g62PoiK2O5L7csXWdeBhdnUiXEtU9B6T0\new1ob34+P/p42prS0KBI+FVX3UA22wWUEaLAxImTueyyf2XatHnU1qr99DF0FLqhQRFrx4HVq7dy\n9dXX09ExgGFk8bwEyeR4Tj31FBYunEc8rvatq/OQUpBOKyGhVyukhGi0wM0338OKFX9DSgfDqBCL\nFfnIRz7N0UcfQWOjoL/fTxLWj85ORaTr6tQ4huFbbnRytrYqlUrqubZWCQEplcjJ5/1GYoODrdx5\n559pbd2EECWEcDAMk0MOOY2FC4/DsuIjFaFAXYNiUV3T2lpoa4Nstp9HH32MtWufRogMplmDYRRZ\nuHAeX/jCVygUGgiFFOnv71diQEple9KrJ/X1JX772+u48cZbcV1zZIXhgAMO4aqrfsycOTPV3Mlk\nIatItW3ZJG2JJyCcsEc4+MsF8/X8QYCdtJVFaCzh1+o1HPbLX4Ffkmh4Ysk3upw21jP3WscZa1MK\nEo0DBHjHEYiCAAECBHgT0NxFSsWDNImvLguqt3u53gRj3+uVDZ0jkMv5UXgdlRfCFxOmCVIOct11\n1/Pww8uGVwcGEKLMaaedwWc/+yXq6+Mj+9i2ioLncv6YnqdWJB588FFuuukWyuUeTBNct55Zsw7h\ntNNOZMaMegxDbRePQ6UiCIXkSD8FXepz165dXHXVNWzblsEwSoCkpWUa55//RcaPn0hNjSLPuqGZ\nLs9aKPjip6fHL92aSimCLoT6Ttfl9zw/lyAUUtvrakye182yZYtZvVp3Jq5gGANMmjSJ8877HOXy\nDDxPnYfOo+jrU0Q+nVbH6ujweOGFlTz88GNkMnmgiBAGyWQtF1zwKQ4+eCHlsiCVUiJEe/9N0y8z\n6nkwNPQC37r427Rt20ZEpoa7TVt88Yvn8cWLPkWsRnUys3EoZTNVZaRK1CaBsMAZnkw2DnZ1y+uq\nSWbjYOMM+4TGfK8TUfR+WkHpGqZaGWlx8GYIuc6mHjvZXw2BEAgQ4P8rAlEQIECA9z3eCWeCJvc6\nEVh/NjZRWAdpq5tHVVuQdBWi6qZT1dDf6c/jcdixYxs33HAVXV3rkDKFEBUaG8fx3e9+nYMOOhgh\nVG18HRzWFYu07aa9HTo7i9x22x0sWfIgppkHCiQSFuec82n23XchnidGzjMWU7yvvV0gpcBxVFS+\nowOefHIj1113LZlMBSgipcuCBQfz6U9/CiHimKYi35mMJsyj+2FFIv4qSSbDqKTfujq/74LrSorF\nIYrFEoODWYrFEj09WdrbS7S29tLbux3oBWzAwbIGOP74wznppI8Ti9XS1aXG0knWOoHXNNWxW1s3\n8otf/JmtW3tRHZKzgOTAAw/moos+TjhcP7KtDow3NKhz6+90sKRDKFTg7vtu5557biFMF4IoabfI\nB2fsyVcu/izT5s/DK1Qg5gD2MKGvCpZbjJB8W+cPjG15LaVfIklPOJ1ZXT3xNOHWZF2vElRP0qrX\ncuzEe62ozqavPm5A9gMEeNcjEAUBAgR43+PtdibsrgSpEH5DKW0Jsm0VidYrApqX6bwCXexF8zbd\ngTYeV9xPE9DGRvVdezssXfo3br/9V5TLqumWlGWOO+4wLrzwy6TTtQwOKrKr7TE6HyGdVs/9/bBu\nXSfXX38V27a14XkpAKZM2ZNLL/0q9fV70N/PKKtPUxOMG6eSmV3XGKkC9OSTz3D11TdSKrmAhWUZ\nnHLKeRxwwOEUi4IJE9RKQi7nB6bVioMSCrkcI9YkfR1TKZdKpZeNG7sZHOyku7uLoaHn2bnTo1QS\ngAuEEKIP8JCyBpiAqnAURv2Z68bzMixe/CcefPBR0ulx7LlnC1OmLKSxcSaGUYfnKY69YUMfy5bd\nw1/+sgopU6geCiXS6UkcffTxHHfc3iPnrHsOWNIhV3Rw+iARhZoJHk8/vZSrr76Kjo6dpLwi0ahk\nQkzwuVNO5Khjj8WOR5C5DFKAXS9guB6QnbCxX07BVif26vf5vJ9oUN2VTE86/d1bMeFfLVu/Onmn\nuplZIAgCBHhPIBAFAQIE+IfA211xKJn0I9668o626+jGYxpjE5/Bd26MDfIK4VeB1PuqijpFfvGL\nX3L//U+gSHGReFzwzW/+MyeeePxIzwJQ3HDjRkXoczldRUdVMHr88dXceOOtFAqdKBJtcMghh3PB\nBedSV6dIppR+OU1N3Ds6oKfHRAhJby8sXvwYv/zlrbiuh5QxEokWTj/9TPbff+pIAzP9PLa3A6gx\ni8UMra1d9PX1k893MjDQwcBAD5WKrsnpAAVgAKhF/QmzgDxSiuE+Bs7w92EgBQwBRQyjF1V+NcrA\nQAdr1mxj5cqtGEYdU6fO5vDDj6Cjo4MHH3yQQuFFpEwALqFQnPnzF3Dgfh+kNm6RLPfh9UE4auMW\nbNJRB6OQwRAQkiC7X+Da6/+P5au24hmCpniBGDn2PeAAvnH2p2mIJ8CqQCGHFBAxC9iE/RuuL0h1\nx7NqaGKul5F0voD2rmnFqXsO6O/0+GNf707RAkIr1ZebtLvL1g/KigYI8J5GIAoCBAgQ4C2ADshW\nB1JfjiONfT82qAujOWH153198MILL/K1r13C8893YRhhoMyUKeP57ncvZ/r0PUdyDsAv0dnb64uT\nchlKJZc77riT3//+YaQMAUkMo45Fi07ktNMOpaVFjDQKc11ltdF2m95eNWa5LACDu+56iJtvvgsV\nVY/S2DieM874LOPGNRIKqf1LJbWfrq1vmpDJFNm48UU2bdpJe3s7u3YNoIi8RJH5bmAQ9acqAnhA\nZngbdU0qlTKe1wOE8bwiIEgma5kyZRLpdB2mWcR1p+G6E+jrW0tbWzflsomUYUKhfjyvzNatQ2zb\n9jRClPG8GBHDI0SWvfaayiknHkuolCZq9BK2LKKGRdoGvBKpokO8mMOLxLDtAg/+8VYeuuc2XLdI\nRFRwZJnaRIILL7yIUz/2MUR3N06uhDNUgGgUO+JgR+2Xdr3TS0b6xo9VUaWS37K5tval9p/q3IHq\nZ13SSbe7jsf9JaRXU80B4Q8Q4H2PQBQECBDgXYd3qqfAW42x57o7HlWNscFXnXSr7TPVnFDbje6+\n+29cdtmPyGa7gQieZ3LkkUfxpS9dQE1NYmTcwUE1phYBGqEQ9PT08uMf/4BnnlkHJBDCpq6ulkWL\nzmPixD3p7/f7TRWL/rnpevq643KpZNLWto0//emvQBPgMGFCM+ee+1mKxfqRLsvhsO7AW6G3dxtr\n1mxi/foXefHFzRSLAhiPIv0R1EpACW0LAo90OkFjYzONjeNpbGykp8fl+ee3MjjYhmoc1gj0MXmy\nzSc+cRq1tUcxNGQQjfp2JN2JWMoKQ0MvsnLlM/ztb8vYtqGfEEWELFImikeecbVJzjjjVPYfP4G6\nUjvF8k7suhSmBSk7RtoUyFKZ2jqL3JDDiqef5vZbrqKtawsJt4TjeZSE4NTTTuPCr3yF+uZmdeHi\ncWwpsYULUdtvT6yJfPXk2d0KAaiLqUtHVS8/6WWl6qxvKZWK1EtZ2ay6oVGV2DySGT32WG8UQVnR\nAAHe0whEQYAAAd5VeLt6Crzcsd5O8fFKHEkT/bGf69c6YbmvTwV2s1mXm276NTfffBNSWkiZJBRK\nct555/Dxj5+C64oRjlguq8TcYlFxyFBIWYZUp+At/Ou/XklXVzdQxjBgn30O4bTTvoBl1Q9bcPw+\nCDohWecTgC8OWluHeOihp4AGoExLSwMf//jpCFGDYUChIOnubmfXrk28+OJzbN26fLgyUBgptd9f\nCwI1eE1NHZMm1TB1aoIJE+poamqgrq6OUqnAk0+u4amn1tLdXUDZhtRFS6VqOfnkEznllMOJxWy2\nbfN7FGj+rJ01Ic8jWoyTKlSQHS+QEgVMmaYGh5jRjhGymTHrME74wAQSPTuo5LOk5CChrGoyFo2k\nCbkRkrUW+UwPv/rFz3l6+XIiQFQIXGCPefP4zne+w5w5c5Qqqb6psZjfKrq21m8CpkWBXjaKx3fT\nlGAYtbXqe90YQ08k/Vo3vajOeNcCITTmz342+9pEwWsh/EFZ0QAB3tMIREGAAAHeVXinXArvhPh4\nNY6kS4K+XM5mJqM42/btvfznf17Fs88+hxBJDCNDY2MTF198GXPn7k1trSLA+bzinOWyn+NQbUtv\na3uO73zn2wwN5ZHSQogQp556NscddyblskkiocRELqf21zZ1zVeVXUfZgTo7e3nssUdx3RAQJ50O\nc+KJH8GyPDZuXM6GDdvYtm0XuVwG1fxsgFAoi5QhhIgiZRyIUlNbWFpdAAAgAElEQVTTQEvLRJqb\n9yAabUCIehoaYNYsFdAul3tYvPh3/PWvW8nnXdQKgmq2YJoNHHTQDI44Yj/q6xOEQrBzp+Lc+ren\n0+qck0mIywxbnnyQJQ8spquvnWS0n8ZEiZrKIJMbGunp7SEkPbrX3En70yZHzZwMdS6VtIEzmCdU\nLhGWLq6V4KHf/Y4H7v8jTrlMrRBUhGBcKsUnv/IVTjn3XIzqElPayyWEUlSRiN/MAvx21zA6p2B3\nybpjk421aquuOqSz0vVx9f4DAy8VBa8Vr5XwB0IgQID3LAJRECBAgH9IvFPi45W4U12dIq/V1SR1\nUrEmtStXPsell/4PXV0DqIh6jHnz9uWSS/6FVKqemhrFPSsV3/eva/cnk+p9qQTLlj3DL37xNYrF\nXgzDJhKZyoUXfpfp0+ePlCYFTcQVJzVN1SMgkVDBZ9f1RcEf//gIhcIQYGPbgn322YvFix+ntXUT\nEEfZenYhRAEhyoCL5yWoq2ti+vS5TJo0i8mTZ+A49di2+r1DQ9r5ItmwYQvLli1jw4alSGkOJ/7G\nAQ8hKuy33z7MnTuP8ePricWUkOnuVs3PwmEICwdZdJAGiJBNd9sLLL7hFryOtURcwdRIBYs8E1Nh\nTjrmWGbP2YfH/3QjT698mrIrWLXsXj7+wS/hFEpQUwvxIrJismrjKm742b2UutuJeh5hIXCFYP7H\nPsa5F1xAXXOzunCRyO4j6boDW3XpTm33yeX8JQ3tI9MKb2x+gB4vkVDbjBULQiiFCH7eQDzuL/do\nvB7rUED4AwR4XyMQBQECBHhX4R/BlqzdIvqhobmgco1Ibr31Tn74w1twHBdQ35122hmceeZZ1NYq\nFp/L+SsOOgiczyvuZxiKn65cuZTrrrsS6EbKMOn0BC655L/ZY4+ZZDJKOOggs2n6AWrTVOPoZmN9\nfYqztrZuZd26J1H2nQYcp8yTTz4NGKia/tra00QsNsTeezcxc+bezJkzg3R6ItmsGOmbpe+1siv1\nsmPHNv7ylzUMDm4GSgghhoPnJqlUA/vuux9NTXuRiiSojTg0RLKEY6ptVyzkUBNyMB0HQ0gaJ9r0\n9W3nsQf/RvuOp2mUQ0ygn6hRIWFbHHvQPA6bNgUjVUPEdjj56IWsWfI3xiHoWLeWXG6A2ngcLJfW\ngQ5uuesuVq1bh2MYWIAlBNNmzuSc889nxqGHqgtlGErt6XbWerlHE/fa2tFlQrVVqHrS64SORGJ0\nfVadLFwtDKrbXlfvr5eO4nF//2qrEvhZ7AECBAhAIAoCBAjwLsM7ZUt+M+LjzeQiOI6qwpPNKjeH\n542uFKS2yfH1r3+Le+75C57XgGHYxOM1fO1r/8Ls2Qt8f3xIccq+PrVvJKI4aankF5hZufIhbr/9\negxDYJoWLS1T+NGPfkqlsgfd3UoQGIbqfaBXHKJR9XkkosbQjhTLKrBp03r++Md7gDJKADD8GlQZ\nUEF9fSPTprUwfnwLU6eOZ999Derq1BZbtyq+Go3qzsyDPPfcetau3ciWLTuQcgAlLsqAgZRh9tpr\nKnPmHMOEhmm4gwVkLktC9lKTsKkN2URsm0g6TNIqkchkCfW00bmrnaceXMvqrRvIGw61Ikvac6gx\ncxz2wbkcf8yhJIWAvg7CZg57sIQzkGHOuAY2dLQhpGTT5s3M3mMP7rvvPn734IMUpUQaBhEpidfU\n8KlPfYpjDj4YQ7dgjsXURatOBtENxvSE04m/Y5NIsll1QaT08wleblJW768bYFR3wCuXVTOJ6vfV\nQiQQAgECBNgNAlEQIECAdxyvRqrfjBB4rYT95cTH6+nPBC/NRRjr4hgL3c1XJxKXSv57y4LNmzv5\n3vc+w8aNGxCighAhZsyYz2WXXcn48ZNGhEMopOw2lqUsPoOD6lEq6XKhkr/85X4WL74L06wgpcWU\nKfvyk5/8iLq6Jjo61DjJpB+MtizFYVMp9VC9CUo89tg6/v73Z/n739dRKDgYRhFIoCoFmYDFuHGN\ntLQcwPTpe9DQoLr9atdLKKTGrlSU8Bgc7GfDhi2sWfM8mzY9h+sOIUQGiCNEhJA0qY05zJ8/l8OO\nPopUak+M9lbMzhU4+TzJWBkrHsUMx0hEaqivsampqVD0oniVLTz5+B/ZuGYdopRhipFjkAxFo8Kx\nRx/G2SeexfiQge0Wh08spU4um8WuZEkmw9gdLhM92Pbcc9x0++1sy2QwpKRHCKRl8dkPfYizP/1p\navTySqWiLubgoFJYvb1+CSjP8208uhSUniBaGermE3q7ZHJ01zttG7JtX2BUT2RfTapzqZ64evnp\n/bbcFiBAgLccgSgIECDAO4q3M8H39Y69uxKib7Q/EyiOV90PSnftrX5d3XfKshQnzGRUcLitbStX\nXHEpvb2dCGFhGAXOOutELrnkSjKZ6IgbRB+vUPDdI7qMaD4PQnjcd99veeKJxzEMAylDzJq1F5de\n+g0sq47OTnVM11UR+2RSPes+WPl8mWeeeY6nnlrB6tXLGRqy8XsFxPC8NMrf38v+++/DQQctJBqd\nQE+PCpTH4+q3pdPKLVNTk+e559bzt79tYe3abbS2diJlGUsWiBlZEP1UDIuSrLDf1DTHzZnL4Qtm\nkqpPY4U8erY+A6V+PG8IGcpTl+/Ay3jE0xaUU6TsFkLdPfzx8adZsvhxwmWPtFfCIo/tlTlmv304\n6ZwzmVmX8ksrhcPqh0ciikgPDEBDA92oVGYTWP744xRMEwyDkhDsPX8+3/r3f2fW5Ml+i+faWjVm\noaCynPv61JipqmNZll9CVAg/4Vd3hfM8/6ZW24OKRX/CFIu+yhqboa6FQrUQCBAgQIDXiUAUBAgQ\n4B3F25Hgq3mTLtgylsS/HlvQK52bPsZYDqY/y+UU18tm/d5SlqV4XijkB4m15TsWUxwxFIItW57n\ne9+7jHy+CyEsLAu+972f8LGPfWJU0LlaEOgiM5mM4oyFgvp+8eI/s2TJMkyzApTYd9/5XHTRhUCc\ngQG1T6UC48f7QeZCQbJ58wYefvhBlixZSyZTxPMMVCOxClCPqha0J5lMGdfNAzVs2tSHYWylpiZP\nOCwIhSQvvpjDcdoZHNxBLreKnTu3DQexG/C8FkASFhmSIkvMK7DPjFoOPmIB+x9yBBMjNrYsETbL\nlDp3YpULJMsDlEyXsttL0fMIZ7uJuRkiwoaBLM8/u4snV68mW3JowEUKl0avxIxp0zjylFOYPmkS\nyLJSZk1Nvv/e85QaMk0oldje08OOtjbyQhCREgPIAeNrajjzwgs5/rTTEHV1SlDoC5fLqQufzarx\ndKKILgVl234GeCzm9yXQEwH8ZRSdG1BdPrS6yYQWAzoPQW9b3X04mRydXwCBSAgQIMBrwusSBY88\n8gjf+MY3WLVq1chnzz33HKeffvpLtj3vvPP45je/CYDjOPzwhz/k/vvvJ5/Pc9hhh3HppZfSpD2P\nAQIECPA6MNaiozmQbvYKb18JU+0YGbuioHtF9ferbQuFEa4JKH4nhF8AptrL/9xzT/Cf/3k5pVIZ\nIcrE43D99dex//6HMzCgAstSqjEcR3HLQkE14xoagvZ2v8nY2rVLWbLkUQzDA/IsXLiA8867kHjc\nGkkqDoVUMDsWg/b2IR57bCVPPPEkra0rh/erYBgVoAbXTdPQMIm9996f8eP3IxabSG/vZv74x7sB\ng2zW5Jln1gAbUE3HBoEKNtuwyWPbO/C8FFIYGEYW6CQUCvPBqRM4cm4zCz84hb1nT6CMhSwUsBjC\nLDtYmSGwymBWyBsZwiEPwkPIXC/IHK4zwPY1W9mycws5CkRQ0iUFTJg4iRMOO4wZ06er5Fqd6Ou6\nKoJfV+cvkzgOu3bu5N477mDF0qXUSIk7/EtylsWxH/sYHz/rLFIzZjDcfEEptIEBf9x8XimzaFTd\nqEpF3RhN1qs9/dUNx3TVIS0QqkuQ5nKjBYGehLub7OHw6DyBt6ABh5QSOVZcBAgQ4H2N1ywKVq1a\nxTe+8Y2XfP7CCy8QjUa56aabRn1eTfgvv/xyHn30Ub797W8TjUb58Y9/zBe+8AXuvvtuDN0FJ0CA\nAP8QeLPVhcZafKpr6Ouxq6P7rzd5WNt/xu5fzbH0e10eXjs4LEvxQ+32ME2/b0C1FVyLg0QCliy5\nhSuv/Dc8L4ZhQH19khtuuIYPfGAO2azvMtGVhYpFf1Wgvl4dc/JkaGuD9evX8fvf/3a4Yk+GBQvm\ncNFFF+E4JpmMH1ju7pZ0dGxm5crHWL78OQoFFykNhGjC8ySm2UNDQzP77XccM2ceRCq1J44j6O5W\nv6GlZQZHHLGQpUuX4rrDfnqKWOSpIUMNbZh0MiSKSCSpUIlJU5qZPfdA5szZhxlNTTQOdlIbGsKz\nIFwaIGxbyP4uQCI8oGsnwilghy2StTZ0duEY/fTnWtm4eSPrd+4ghksFvw/y+IYGTjj0UPY58ECE\nrs2qSXcioS7c9u3Q3Ay2Ta/nccftt/OX++8n5DhEhKBdCMqmSbqujit+/GMmTZjgt0XWCRulku8V\n0ysDumyTZSlBAP7NS6dVZaJqQaC39ZdqdOa1eh+P+0tfevLpbavHcRzfo6bxFmTnB4IgQIB/PLyq\nKHAch5tuuon//d//JRaLUR4TudiwYQN77bUX8+bN2+3+ra2t3HvvvfzoRz/ipJNOAmDWrFmceOKJ\nPPLIIxx33HFvwc8IECDAewVvtrpQdZPW6jzMujp/HN236bWOXb0KIKXfKHZ3DWV3F4TVQVu9ra7g\nU90nKpn0zxfA8yQ/+cm1/OpX/w44CFFi8uTp3HrrDUydusdL+KPORTVNNbYQivPqAHNf3w5uuukq\nhOjDNLNMnz6Zr3/9YgzDHOlBsGPHAM88s5ZVq5bS1rZ1uP6/h2EIhHAJh6PMmTOfBQsOYs6c6UQi\nBkNDyiqfzfq8NWo6HPuBmeydgva2btqHCmRLJaIu1IcFLbUe45v3Yq9ZcSbN2oPa5ilEjRj5ikU0\n14W1dR1mXycxL0OoNolt1UIyhZPth95BRBgSlLBFCTp3QTrNji0bWb9iBbva2iijbD0VIA0kams5\nZOFC5uy9N6Ym7TrDeXBQXTTdorlcZmjLFv66ciX3/u1vDDgO0vMoGgZFoDRMhi846ywmtbTAhAnq\nAnd1qTFA3fDBQf/i19T4qlAnC6fTo0uH6htYvWqgKxWZpm9J0k0bKhX/Ydt+Z+Sx5Uf1RHylCfpm\n8Ha3/Q4QIMC7Bq8qCpYsWcK1117LJZdcQn9/PzfccMOo7zds2MDMmTNfdv9ly5YBcNRRR418NmXK\nFKZPn84TTzwRiIIAAf4B8Wa5RfVqgbbUVBdo0fmYr2e8sec2tmCL5mpjbUP6XLSQsG3FC6sDvMmk\nspL396tAc1+fyw9+8Ev+/Oc/IEQjodAu9t13OvfccwdNTU2j8hY0GR8cVGNFo2r8hgbVqCufh2y2\nl2uu+XeKxS6EKNPUVMM3v/lvhMNxQiHYuHETDzzwCEuXtlIqucOVfsrDTcViTJxYz8EHL2T27A+Q\nTteOiI5iUblsymWQJYcJkSwRL4tdKRHPOUyz2tlvRoFQag9KoRg1hV3UmkNMqGvES9RQPyVGbFwN\nIhmi2DuAPTCAuWML5mAfsZALboVkrgN6HBxDEA552LaDXcpDoYCUkq2bN7P273+no7OTAlBEdUEo\nAhMnTeLAD3yA6TNnIqLR0TdG34h8XpFw16VYKLB6xQoeWbaMtkKBGFAQgpAQNEycyAsvvkjIMMjb\nNvMXLRq99KOXe3SHtUxGEXldWskw/GMmEsqmpAm/hm5Iph/VScVajWazvnKsVZ2bR5Kiqydd9eSq\nVtpvZRb/21kVIECAAO86vKoomDt3Lo8++iiJRIKf//znL/l+48aNhMNhFi1axObNm5kwYQJf/vKX\nWbRoEQDbtm2jsbGRiC54PYxJkyaxbdu2t+hnBAgQ4B8FmpxXvw+HFU8Lh31SX90Y7K3o0aSFQnVO\np+ZxOnBrWX55eJ3MC4ovhsNq3/b2Epde+jNWrPjrsH8/zSGH7MNNN/0fTU3JkWOVSoobaiGRTPq5\np5WKL0IqlSLf//5ldHXtQAgIh6NcdNG/43lpHn74CR555D7Wr98GRPC8FEJ4GEaOaNRlwYKDOPTQ\n45kxYyYDA4JiESzpIHIOpg2uaWO6UG9k8bxeaqIOtpfHzg3QKAcR6TYqrqBGlDGHBohQJGSFSQxl\nsYqtWF4NwpyCPQAUHaIdL0LvTsDAtmzsSAh7qAimh00JYiHoKeH29bF5yxZWrllDf08PNqoikI2q\nCjRu2jTmH388e86cqW5spaLIdzarVEwk4idxTJ5MpVxmxfLlLH74YYp9fQiU5UgC+7S0cMKZZ3LP\ngw8SEYKilHz4+OOpTaV8IaATScplX4WCn0gci6nVguZmdT7ZrFJs4Ef49eTRk1N/pzsS61WCl6uD\nqy1QeqzqyV693Vi8wSx+IQSG675l4wUIEODdj1cVBc3NzS/7XWdnJwMDA7S2tnLxxReTSqW47777\n+Na3vgXAokWLyOVyxPRyahVisRgdulB2gAABArxGaBFQHbTUhF1zpmzWt3WD//qVhIHmaS/Hy/R3\negWgs9MPIDc2+rZzHfhNJBRX1OS9txf6+hwuu+w/Wb78BSCF5xU47rj5/M//XEIyaY86juMoQVCp\njE4Ork5otm24++5fs27dGlw3jRB1nHbaF1i27FmWLPkRfX0mYCNEPUJkEaKLKVNmctxxp3DCCQuI\nmlGMikM2myNlQNhziHsZQtKhUhQIp0TcEzSUB4iFegjnB4k5g9hujppSL3lZoOJZNFkRQqVeKpEE\nhmMQqRQQlQyDO4u0vvA0hVIRT3iEh/ox83nCUhKKRkk3NJCOxahracEOheh1HNY/8QQ7Vq0ik81S\nQdmEQImB6dOmse/s2TRNnaoIeCrlR8rzeSUIpFRJwKaJrK1l2caN3HPrrfTt3Em9lJRQ4qKpro7D\nTz2VBfvtx7ItW3h240Y8IbBsm7M/+Un/ZhcK6gS0dUh3H45G1evGRr96kIYWBvm8UoS6BFW57HeC\nA1/djs0JgNF5A2Mn4TtAykW1+g0QIMA/BN5USdKamhp+/etfM3PmTOrr6wFYsGABXV1dXHXVVSxa\ntAgp5cv+5/JGk4zXr1//hs85wLsTheE/vMG9ff/hrby3+v+SclngOKP/X7FtOcKVursNPG/0vqrS\nz5gP0UFYMVwmVAyP5QGCfF69j8UksZgicrmcIJcTDA4aw+cCHR0S25aAIBaTCKFeV/cj6Ovz+OEP\nr2b58i1ADLA48shDOOusk3n++RfJZFxiMTlSeGbXLpPt27X1RACSSESO2NtdF3p7u7jttnupVJqA\nJE1NU/nNb/6M54VRJUQBwggRZc6cuRx11BxmzJiILFUotG3GtEvUNoUQRgUrn6dnp0M5JImEy9CX\nIdzfRYgy4ZhQZD7Tj+UVCHt5jP5uElGTUjFPz6YCpUyGoVKWwUKOSjZLsVymC1WLyAUcVLszA6iD\nkbyALsCJRqmLxTAGBki6LkrKqH1ylsXMPfZg3owZJBIJKkD74CAyn6dSKiHTaaRlIXI5KJUQtg2u\nS+vGjdz/2GOsf/FFhGFQCoVwKhUaUikOPegg5h50EGY6zeaeHq6/5RYMIRgQgpOPP54csGXTJnAc\nPNtGhEIYfX1QLiOTSWQo5CcCDyejSCFgcBA5PAlFLoeoVJDDtiZp24hCAVG9tFQuI1wXr6EBz7Iw\nhmvVSikRVQkp0raR8firiwHHUeNXQY4RETqB+NVIf6FQQLguGzduHJV07I2t+RvgPYfg7+37F/re\nvlG8KVEQDodZsGDBSz4/7LDDeOKJJ8jn8yQSCXLaH1mFXC5HUnskAwQIEOAVIIQYRWIUJ5EjJN6y\nfEEgpcTzPKQUuyU+KrovkFISCnlVYwgsS1IuQy5nDPcAUN95nuJx8bgctuwIbFsOrwII+vsNIhFJ\nfb03IgIqFUm5rMZxXZef/OQaVq5cg+raW+S44w7n5JNPHg4kuziOEhuVikRKg95egZRQLBpYlkel\nYlCpeEyY4FIsGpim5Pbbr6dScZGyHiktdu3qR8o0FmFsXJIJyZQZ+zL/kFlMmwRepkC6dwd1iRIJ\nUSEsPOzeMnWyTKkxRay/h1BXN1ZbP5aTwwgZGOEQEa+CmxskVMlTLuXoG9hFcWcr5d4eisMVgCSq\nnVkJ5ffX8isBDKD+2JSGtwMlFPqBF00Ty3Ewi0VSUuKgBEE5FmPKfvsx/4MfJAp4tk05l8PI55Gu\ni2EYGLkcXqWCEQ6rccNhOtrbWfLww7Ru2kQZSBsGOc8jkkwy/+STOf6YYwi7LlJKXCG4+3e/I9vX\nB4ZBOpXiox/5iE/my2WM4eUgLxpFDBNiOfy3S2QyiEoFLxZTwkRKRFXvAc+2GZmBw83LpOepbcpl\n9WwYSCkxSiVF4IePKysVJQUtC6ltSq8COby6oM9B7obAv5YVAF2O1DNNXNN8xfECBAjw/sGbEgXb\ntm3jqaee4vTTT8eu+o+iVCoRjUaJxWJMnTqVnp4eHMcZtc3OnTv54Ac/+IaOO3v27Ddz2gHehdAR\ni+Devv/w/+PeTpw42j7kOMp6oysF6c/AbzRbXSRGuzyqi64kEr7Lo65udCUh3Wm4qWm0hclxIJ93\nufTS/+Cpp9YjhMA0c5xxxumce+6XcV1Bfb2yGenAc0eHcsWUSuo4+lxCIbXaMXcu9PWVuPrq63j2\n2U2otFuwcbDJYRJhzwmNzJu9Nx+Y00I0GcOsSdLSVMIaKlPjChKmR6iYJRWLQCqBbQmcsqA57EAk\nj1EuEUqEcIVJqZShY9cu2rdtZ6BtO/n8EAYuFtCEXsNgpLZ/avj9ENBQX0+6vh6zuRlLCMxQiPLA\nANu7uljf1UWv6xIVgrDn4QhBL1CRkkQoxGfOO49pM2cqe45OGG5vVzc2l1M3wzTBdZGpFBu2buUv\njz7K8xs24EqJJQQWkI9EOPEjH+Fjn/gE9VOm+MkdnsdDd93FU6tWYRgGOSH47re+xZw5c/wb2dqq\njiWEqiakKwHpBODq3ADVnc2fUGMj+5qMDw2pbXXzCV02Szchq84beLOJMG8Cwf/J718E9/b9i/Xr\n15PXVsc3gDclCjo6Orjyyitpamri2GOPBVSE4eGHH+aAAw4AlJ3IdV0eeeSRkZKk27dvZ/PmzXz1\nq199M4cPECBAgN1iLDEPh0e7PcDna7oL8dj+A9UuD03+qzsY647FoMavqRmd5KzGklx22U956KGV\nw2cmOfXUj/K5z30ZEDQ2+hUmBwbUueXz/sqE5qCepytdZrjppt9z00330NU1BFgIUcCSfaTCtRx9\n4DwOnjKDpkQKz45i17jErT5CA9upC1ukwg6hbA+RXAa7UoBIAtuqgIxhD/SRjBfIDpVoX7eJ1p07\n6Op4kb6BXpQZipEGYdrUVACiQE1dHalUippIhHBzMzVTp1JbW4sxrHZy0SjPL1nCytWr2bBjB87w\neGkhqAhBNBSibtw42nbtIlep4FUqXHvPPXznv/6L1Pjx6scPDKhmDJnMSGtnr1xm89atPPzCC6zb\nsYM0UIv6w+YIwZyDD+aEs8+msaVFlVLq7YUpU6C+ntYdO/jh//4vEcCQklOOPZZjDj98dGLI2Ki6\nJvuJhMr61g3GdD8BPdF0FaHqmrbVNp5sVuVC6EpFesIECBAgwP9HvClRcNBBB7H//vtz+eWXMzg4\nSENDA3feeSebNm3itttuA2Dy/2PvzOOrqu+8/z7n7mv2hISEQFgVBQFFxQUVVFxal1pbq1any9SZ\n2tJpn7aP3ev0qTPq2I7W1rZaHbXOaN1ArQsFqyICAiIgEISwBRJCcnNzc9dz7z3n+eN3vzk3EbS1\ntnX0fF6vvLLce373LD/xu3y+n8+YMSxYsIDvfve7JJNJIpEIt9xyC1OmTBlKJBw4cPDhwV9D9vxQ\na4rikMh7CoTdMbKIK/FfOKyGiCUx8Hjs1yTgF7l5WS+btZWIwmE7objrrjt59tnH0bQIUODCC8/l\nK19ZOETh8HjshKO7WxW+3W7bcyuTUd9Ns4/f/e4Jnln83xjJA1iWizA64CZkDnLpWTM544RziASj\nuNIJgvluAlaGTD6AkYJIYj+eYhBf0I3XNHC7C0QCRfAEMIoFCukke9eu5M1XX6F9yxaKmQwVKP6/\nF9WLyKGSAL/LRUNrK6PHjKHG46GiWKShslK1YkxTtWkqKshmMmzZu5fN69axtaMDXybDIJDXNApA\nXtdpqa3lxDlzOH72bGqqq9mxbRu//vnP6c1m2dndzep165hfUWFnaiV34nw+zxvr1rFu1Sq6YzEG\ngJymQWmGbdL06cydN08Zj+Xz9lR2aSNkEwkWLlxIIpVC1zQampr47Oc/r14XYYz+fvvhlPsLRCKU\nOGbqYYkXgsiSSpYnJhLhsN0NkE0qm1M2pshNjdTA/Uv/I3DgwIGDPwN/VlIwkter6zq/+MUvuOWW\nW7j11luJx+NMnTqV3/zmNxx55JFD77vhhhu44YYbuPnmmzFNkzlz5vCd73zHUTdw4OBDhr+G7Pmf\ns+ah/nYokRcRnin/J0rTbMqQ16s6A2JwWyyq13p7Vczo88Gjjz7Fz3/+W0RVf8GCs7n22q9RKGhU\nVNhKlRLHFQpqnWBQ0coLBejp6ebRRx/i6acfppBO4Nc8uHQf/kKOsJYl5HMxvrmFr119Ea7eblyF\nLhjsxUql0RJx8GtovjC4UlgxN/SaaBUhwlE/bt1H+9aNrNq4kRVr1+Lp76cKiKD+x6CVztwEapua\naBgzhtFjx9LS0IC7ZMSwd2AAs7t7yE3N0HW2d3ez+qWX2LRxI8F0ekhG1Fta2wVMmDWL4086iSPG\njEGvq1NZVT7P+Joazjj9dB5++mmqikW2rVnD/Fmz1E1PpUibJi9v2cKrS5ZgxGJYqLkENxDw+Zg2\ncyannnMOo1tb4eBB21WuokIlLbW1WMEg3/6Xf2HD2rVUatrVsc8AACAASURBVBo5l4uvfulLRCxL\nVf99PuF92RtEOP2SbRqGrUIEdiIhD1SyyWBwOF9tpJW3bDCfb7gC0btx9HP8BBw4cPAX4s9KCq69\n9lquvfbaYX+rrKzk+uuvf9vjAoEA119//Tu+z4EDBx9svIcy6n/ymlKElZ8lphOWiKYNV4lMpVS8\nJ0yRVEoVgg1DxYzxuHqfptlmZuVJRH8/bNv2Gt/61o+wLMVjmjZtFldd9VX6+3WCQRUrShE67DXw\nYpALQNb0gu4lFtvOf//3fTzzzO8xzRgeC0a7LcLeSkaNaqS1sZZX16+k2koxd2oL4cFOfAP78A7E\nbAnNYgISRcjvV0GxrmNl0+w+MMDa3bv545499MTjhC2LIooGpKPoQc2RCG0tLYweO5bG0aMJJBJq\nXTHokovVNIo+H28ODrJx507eaG+nP5XCAnzAqNKaOhCIRJg4bRoTp02jZuJElQGBqqaL5mptLaNm\nziT57LOMKhaJ9/SAy0X/gQOsefpplq5cSU8ySaVpYqIGmt3BIHPnzOHkBQuoGTtWrZNM2pbS6bQa\n0qipAU3jlzfdxLInnsCtaVjAF665hiNnzrSzw1TKzsxyOTUDkM3C6NG2/GkySUmuyt4MpaFhUim1\nVnm2eahNKngv5gb+Gv9hOXDg4EOHv4g+5MCBAwfvd5TTuGW+oFz4LJ9XMZ1QdsqLtPm8bWibSqnq\nvRjYSnHY41FFaFEd2rWrky996TvkcgaalqCxcSaf/ew3cbk8RP0GWs5AjxvoXijkQXPnCAcNmt15\n1m3q4K7HnmLpqheI6gZjfCZR0yJg6kxsHcNZH72E42bMZPH9d7AtdYBwUaPeSOA70Ik3lYDdu9VJ\niZtbJgO5HL2pFNu3b2fv3r3E0mkOogaDq1ABewVQVVHBMVOmcOS0aYxrbkYX595kUgXFovFaKGDE\n4+zu7+eVjRvp2bYNK50mi5IQrUYlBB6gMRCgefRoWseNo66hAa2y0r754bBaM50eGvzF6yWt6+R0\nnUHLotHr5YEHHmDt0qX4MxlygLtUXXdFo8z/yEeYd8IJVFVWqqA/EFAPyuVSDwUUnSkaBY+HZxct\n4qE77sADBEyTT15wAZ+cO9c2D5Mh4YEBtYZp2n4EMngi3DDhi8mxlZXqeOGXlWelMLya79B7HDhw\n8D6EkxQ4cODgb4byqn353/4Wa5YXby3LZomUzxyUF1y9XhUL7ts3PCGQ94ZCKuZOp1XMHApBPm/w\nrW/9gHg8hWX5qaoK893vfpsqr4fKwT0EMwYuv4fsgIdIHYQ8achl2PT6azz40MNsfr0dQ9Np9Vno\nFAhaSU6aMJ4Lz1jAlKNmovvdeHM91FoFGosGvmIObdcWvKOrbR5TyXk3V1PDrt276dy6ld7+ftwo\nX4Ag6h/+EOANhWidMYNjx45l3KhRuKSVEgqptQYGwO2mGIlwoKuL7s5O3nz5ZTr27iVumgRRHQZJ\nArJAJBplVkMDrXV1jKqsVLr50nJpaIApU4ZPUkulPB4Hr5et27YRMk0006TjjTfYsmULLcUibpS3\nwbiqKuacdhrHzZtHcNQo9QBqatTamcwQFQm3WwX2LhckEmzcsIE7b7wR3TQJaRpnzJzJv3zxi2h7\n96rrjEZVR0Uso+X85ByF6iObRAL/TMZuO5UnAOV/MwzVVhJakWyi90qW+6/xH5YDBw4+dHCSAgcO\nHPzNUB4zye/vRVLwTmsKzbv8PeVSn8mkrTwpx+fzlLwK1Htk8Bhsk9qmJnXc3r0Q6zZ4+IE7yLRv\nYryVRfeH+P711zG6pRISCYLkKBYsgtkYeL1E3W7WP72IRY8tZteu7WQ0HVexgNcoUAvMPHU2nz/n\nbI6srVUf7lJqQ/R1M2viaF4qJnEDnRs30jl2LM11deD3czCToWPDBtbt3YtlGFRiqwUVAE3XmXbE\nEUyYPp221lbcNTXKDU04VH4/ViLB/oMH2bN9O/t37uTA7t3o+TxFVODvQs0HhEprVvj9tE6YwPjW\nVpobG9HTaaX043LZXQBQ6w8OqjaL221zrwIBqKxkW3s7zz39NAd1nQhQY5oESglBRV0dZ596KjOO\nOAJPVZUdXFdVqbXAbuFUV6vXEglIJmnfvZtv3XwzqWIRTdc5srmZ//uVr+CJx1VHQahA8qAlYxSX\nYqEElUw6h6g55UF/+RyBKBHJRpSEQL40zdbIfS/w1/gPy4EDBx86OEmBAwcO/qb4a8Qr77TmoeYw\nCwW72CsxoabZ8wQiJFNZqWK6QkG9RwaCS4Vt3G5oaTB4ccmrvPjY/1DvNgj7XHzsgnOYrkNx5yqs\nmlG4yWMULPT+/XTtWM/ty55k3+bNaFoBv2VQ0DQst5uTz5zHZQsW0NbQoCaXu7pUUBqNquA5mWTc\n2LG0NTSQOnCAvGWx6KmnGD9hAun+fvb19KCj6EFxVHXdD4xramJ6Wxtjxo/HGwyqAFfXyXZ305NO\n05tM0rd/P/0HDrBtzx5iAwPUoehAntJaPlRCYAAtkQiVjY2MmTCBaaNH49J1lVz099s3Wm6sz6cq\n+VVV6qZmMupGJpOY6TQbYzF+t3Qpa9aupahp9LlcSgZV05g9dixnzp/PMaNGoctcQyqlquyGoRIO\nTVNJiKwrlXiPh/ZYjO/ffDPuVAoXEK2u5jvf/CaVUuWPRFTyksupByt+BIWCrQVrmuozRV6qHOUm\nFwKRIhXp0nJr6/JN+17CSQQcOHDwF8JJChw4cPChg2GoeFDEYYRKLoVhMTsT/4FyupEkEwCFtIGP\nJMb+DhbfdQMTAyl0y2La5LGcdcJ0vK4cwfxBrLxJejBP3+52ljzyADu3bSGraUSsPKZl4A0GOfv0\n0zn3Yx+j8YgjVOuhp0dxl4pF9YG5nMpOikXcVVV84ooruO+22zANg3SxSHt7OyaKIpRG/eOuR6OM\nbW6mbfRo3H4/xXicLVu3MhCL0VUosL+/n2w8Thq7A2Ch5gyCqCRAfm/2eok2N9M4cSKtkydTUyzS\n3dcHHg8uj0cF1pGIukGi6er320MY4bDtwHbwIAOmyfr163n1lVfY2dtLv6ahuVxkNQ2vZdHS0MBX\nP/95jqqvR3O5YOdOtXY0qtby+9W9KSkfDQXmIhFaLLKjp4d/+9GPiKVSSkUpGuVfb7qJxjFjVFYn\nMk+appKWYFA9cEkshJIl7nLSIYhE1LMA9bNQi0bSiGQzjUwc8nkngHfgwMH7Dk5S4MCBgw88RKK+\nnEIkLA8J9PP5UpfAMsj3GwT8BuEq8IS9hL1eeuLeoeQgGAQjaWD0DeItDnLfr36Du28HUTOPPxTh\nsx+/gEqjFx9AJsn+A3tY9NzTbFz9Cj7TxAP4TYtCKMRpZ53L2R//ODUS+CcSKmDNZodXq4NBFRBb\nFh09Pbzy/PPk83kKwCBqWFichXVUQJ9KJFixeTPtmzejoWRBCyi50SQqCSgRe9SMQem4EBBxuRhV\nX099XR31bW3UZ7NokQjU1g5VzIvhMJYMNedyKlgvFoe7v8kgcan6vmvvXtY8/zzrNmwgm83itiw8\nQEjTKBYKVOo6EZ+PG778ZWoqKmyJJ1EEGhxU6wYC6m91dSoJMU31GaVEZNfGjfz85z9HHxgg7Hbj\niUT41xtvZGJbm0pMikV1jAwRj9SmzedtKdKR7sTSCZDN5PMNH0xJJu0sUtznZM3y4x04cODgfQQn\nKXDgwMEHBsmkbQ4r/lAwfA5TqOPymlCGKioAw0Azcngw8OQG8Q7mIa+h4cHli+AKeFUROQ9a3sDK\nJ9n5xmusXf4cvsIg/jx89sxTqTywB59XJ0eOJ5YvZ+3LL5OxLAKoIDzj8XDerFnMP+ssampq7Awl\nmVQBbjyugtaamqEA2IpEeKOzk6cefZR1mzYRNU0aLItScwMTFey7UUlBCJUo9KC6B6HSeyQJ8KEo\nQTpQXV3NmEiERr+fiooKqisqGOX345KBYHFoKykEiXmXJfQgmeKurbUHNQYGhqRB87kcu9auZe3u\n3Wzt6iKO3ZUwAX8gQM408Zcq9BfPn0+NyHxKK0eyN/EekCFisJWDkknIZNixfTu33nEHB5NJAi4X\noVCI7/3gB0xsaVEtIhlMzuXsLM/rtWVSg0G1Ifr7bRnWcj3bQ3H3JRmQ13I5e32fT52fzBm8VwPG\nDhw4cPAewkkKHDhw8IFAMqmK7AL5+bAy8IbyB/ACWsiLJ+Ql32+g+cCdTOI1kmCk8ebzeCsq8JIn\nlwuCoamCdDpFKNfDf979U3yFfsLFAscefSQzjhxHLpNgxR9e4KXXXmO/aeI3TfyaRs6yOPb44znv\n7LNpFnWcQkHxzjMZddJCN3G7wTAwczne2LKF361axcvbtxMxTQK6jmmaWEDT2LEcd+qpzJw8mURH\nB91dXcR7e0n392NkszSaJkW3G13X0V0ugm43vlGjqKuqonLUKGpDIfyWpYL4dNr2DCgW1e8+nzo/\nqaiLzXI2i9s0sQoFe+K6q0u9v6TW0xeLsXPXLnbs2EEyl8NEdSr26Tp1psnExkZmzpzJmldfJd/T\nQ1HTqGpu5owLL1QP1DTtmYRiUQXWwaA9rCtffv+Q8cOmjRu542c/I1YyH/OEQnzjO99h4rRpNkdM\nnOPkOiMRNe8gLsWgfvd67e6DVPcPZxImX5K0lL9HHO3K3+vAgQMH7zM4SYEDBw7ec5QXU/8WHkqG\nYTNIPB7wYqDlDVIZCANGErwWeL1evF4vuUGDfGwQr1edaFgDK+fDmy8F5AM9eM0cFEpGYJaFt1iE\nmka0gobmCYHHpH3Vi7y5eS0BDMJeF+d9dB4vvL6aPz7/PN5UijyqQq8BbVOn8pELL6StqUkF2YGA\nrYXf1aUceCMRVZF3uchrGq91dPDEypV07dlDUtOIAANuN3ng1NmzOfP445nQ3Kx48NksdfX11EWj\nMH686jYMDqpgtKZGBbniOSB0mGRyaE6BbHb4A6uqUoPO2awKlINBu2JfCsJdMugbDg8N6xaKR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iidQUCurzTVMdH4nYykiFAmQyZItFVrz8Mi+sWUMGJVMaBurr6znz1FNpCofVNR84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\niivQBwdV4CsKQZalzs3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pqoLf5VKz/xJcnUwqMRIKOZl/JEgZ6Azkbm1qwvMPPIBnVq9G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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a13fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_clusters=3\n",
    "clfgmm3 = GMM(n_components=n_clusters, covariance_type=\"tied\")\n",
    "clfgmm3.fit(Xall)\n",
    "print clfgmm\n",
    "gmm_means=clfgmm3.means_\n",
    "gmm_covar=clfgmm3.covars_\n",
    "print gmm_means, gmm_covar\n",
    "plt.figure()\n",
    "ax=plt.gca()\n",
    "plot_ellipse(ax, gmm_means[0], gmm_covar, 'k')\n",
    "plot_ellipse(ax, gmm_means[1], gmm_covar, 'k')\n",
    "plot_ellipse(ax, gmm_means[2], gmm_covar, 'k')\n",
    "gmm_labels=clfgmm3.predict(Xall)\n",
    "for k, col in zip(range(n_clusters), ['blue','red', 'green']):\n",
    "    my_members = gmm_labels == k\n",
    "    ax.plot(Xall[my_members, 0], Xall[my_members, 1], 'w',\n",
    "            markerfacecolor=col, marker='.', alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is better? Unless we have some prior knowledge, we dont know, and rely on intuition and goodness of fit estimates standard in statistics. But thinking more about how we might use prior knowledge takes us into semi-supervized learning and such, and also evaluation measures for clustering, which is not what this lab is about. "
   ]
  }
 ],
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